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Using Depth Neural Network (DNN) modeling technology, a prediction model of Doppler spectral parameters of sea clutter based on multiple measurement conditions is established based on measured data of sea clutter from shore-based radar under different radar parameters and marine environmental parameters. The recognition of sea clutter spectral characteristics based on environmental characteristics and independent of clutter data is realized. The spectral frequency shift and broadening prediction accuracy are greater than 90%. Based on the prediction model, an analysis method of Doppler spectrum influence factors based on the parameter cycle decreasing cognition is proposed. The influence of different measurement parameters on the Doppler spectrum prediction of sea clutter is analyzed, and the change law of spectrum parameters with the main influence factors is obtained. The results are of great significance to the application of sea surface target detection based on Doppler characteristics. Using Depth Neural Network (DNN) modeling technology, a prediction model of Doppler spectral parameters of sea clutter based on multiple measurement conditions is established based on measured data of sea clutter from shore-based radar under different radar parameters and marine environmental parameters. The recognition of sea clutter spectral characteristics based on environmental characteristics and independent of clutter data is realized. The spectral frequency shift and broadening prediction accuracy are greater than 90%. Based on the prediction model, an analysis method of Doppler spectrum influence factors based on the parameter cycle decreasing cognition is proposed. The influence of different measurement parameters on the Doppler spectrum prediction of sea clutter is analyzed, and the change law of spectrum parameters with the main influence factors is obtained. The results are of great significance to the application of sea surface target detection based on Doppler characteristics.
Micro-motion clutter typically exhibits significant Doppler broadening, raises the noise floor, and annihilates weak targets, resulting in false alarms and missed detections. Removing micro-motion clutter effectively is critical to improving radar performance. In this study, a micro-motion clutter removal method based on the cancelation of the Short-Time Fourier Transform (STFT) spectrogram is proposed using the difference in the morphological performance of the constant-speed target echo and micro-motion clutter in the STFT spectrogram. The target echo appears in the STFT spectrogram as a linear energy strip parallel to the time axis on a specific frequency unit, whereas the micro-motion clutter appears as time-varying complex shapes across many frequency units due to its time-varying non-stationary characteristics. When the original STFT spectrogram slides along the time dimension to obtain the new STFT spectrograms, the target echo is distributed in the same position, whereas the position of the micro-motion clutter is different. Therefore, subtracting the above spectrograms, the target echo and the micro-motion clutter can be separated based on the intensity changes in each unit of the STFT spectrogram before and after subtraction, and the micro-motion clutter can be removed. The simulation and field experimental results validate the proposed method’s effectiveness. Compared with the common time-frequency-transform-based L-statistics algorithm, the proposed method can remove micro-motion clutter while retaining the target echo. Micro-motion clutter typically exhibits significant Doppler broadening, raises the noise floor, and annihilates weak targets, resulting in false alarms and missed detections. Removing micro-motion clutter effectively is critical to improving radar performance. In this study, a micro-motion clutter removal method based on the cancelation of the Short-Time Fourier Transform (STFT) spectrogram is proposed using the difference in the morphological performance of the constant-speed target echo and micro-motion clutter in the STFT spectrogram. The target echo appears in the STFT spectrogram as a linear energy strip parallel to the time axis on a specific frequency unit, whereas the micro-motion clutter appears as time-varying complex shapes across many frequency units due to its time-varying non-stationary characteristics. When the original STFT spectrogram slides along the time dimension to obtain the new STFT spectrograms, the target echo is distributed in the same position, whereas the position of the micro-motion clutter is different. Therefore, subtracting the above spectrograms, the target echo and the micro-motion clutter can be separated based on the intensity changes in each unit of the STFT spectrogram before and after subtraction, and the micro-motion clutter can be removed. The simulation and field experimental results validate the proposed method’s effectiveness. Compared with the common time-frequency-transform-based L-statistics algorithm, the proposed method can remove micro-motion clutter while retaining the target echo.
Synthetic Aperture Radar (SAR) can acquire high-resolution radar images of region of interest under all-day and all-weather conditions, a capability that has been successfully applied in many fields. In the environment of military confrontation games, complex electromagnetic jamming severely impacts SAR image interpretation and intelligence generation. Scholars have proposed numerous SAR anti-jamming approaches to date. However, the recognition of SAR image jamming types, which is the prerequisite of anti-jamming, has rarely been reported. This work focuses on active jamming type recognition in SAR images. First, five typical active jamming modes are selected and further subdivided into nine jamming types based on various jamming parameters, which serve as the objects of jamming recognition. The typical active jamming datasets are then constructed based on the stacking of simulated jamming signal echoes and real-measured MiniSAR data in the echo domain and SAR imaging processing. Based on the jamming datasets, an attention-combining deep Convolutional Neural Network (CNN) model has been proposed. Thereafter, comparative experiments are performed. Experiments show that, compared with traditional deep CNN models, the proposed method achieves more accurate recognition and more stable performance across various scenes and jamming parameter configurations. Synthetic Aperture Radar (SAR) can acquire high-resolution radar images of region of interest under all-day and all-weather conditions, a capability that has been successfully applied in many fields. In the environment of military confrontation games, complex electromagnetic jamming severely impacts SAR image interpretation and intelligence generation. Scholars have proposed numerous SAR anti-jamming approaches to date. However, the recognition of SAR image jamming types, which is the prerequisite of anti-jamming, has rarely been reported. This work focuses on active jamming type recognition in SAR images. First, five typical active jamming modes are selected and further subdivided into nine jamming types based on various jamming parameters, which serve as the objects of jamming recognition. The typical active jamming datasets are then constructed based on the stacking of simulated jamming signal echoes and real-measured MiniSAR data in the echo domain and SAR imaging processing. Based on the jamming datasets, an attention-combining deep Convolutional Neural Network (CNN) model has been proposed. Thereafter, comparative experiments are performed. Experiments show that, compared with traditional deep CNN models, the proposed method achieves more accurate recognition and more stable performance across various scenes and jamming parameter configurations.
This paper establishes a hybrid distributed Phased-Array Multiple-Input Multiple-Output (PA-MIMO) radar system model, which combines coherent processing gain and spatial diversity gain to synergistically improve the target detection performance. We derive a Likelihood Ratio Test (LRT) detector based on the Neyman-Pearson (NP) criterion for the hybrid distributed PA-MIMO radar system. The coherent processing gain and spatial diversity gain are jointly optimized by implementing subarray-level and array element–level optimal configurations at the transceiver and transmitter ends. Moreover, a Quantum Particle Swarm Optimization-based Stochastic Rounding (SR-QPSO) algorithm is proposed for the integer programming-based configuration model. This algorithm ensures that the optimal array-element configuration strategy is obtained with less iteration and achieves the joint optimization of subarray and array-element levels. Finally, simulations verify that the proposed optimal configuration offers substantial improvements compared to other typical radar systems, with a detection probability of 0.98 and an effective range of 1166.3 km, as well as a considerably improved detection performance. This paper establishes a hybrid distributed Phased-Array Multiple-Input Multiple-Output (PA-MIMO) radar system model, which combines coherent processing gain and spatial diversity gain to synergistically improve the target detection performance. We derive a Likelihood Ratio Test (LRT) detector based on the Neyman-Pearson (NP) criterion for the hybrid distributed PA-MIMO radar system. The coherent processing gain and spatial diversity gain are jointly optimized by implementing subarray-level and array element–level optimal configurations at the transceiver and transmitter ends. Moreover, a Quantum Particle Swarm Optimization-based Stochastic Rounding (SR-QPSO) algorithm is proposed for the integer programming-based configuration model. This algorithm ensures that the optimal array-element configuration strategy is obtained with less iteration and achieves the joint optimization of subarray and array-element levels. Finally, simulations verify that the proposed optimal configuration offers substantial improvements compared to other typical radar systems, with a detection probability of 0.98 and an effective range of 1166.3 km, as well as a considerably improved detection performance.
The multirotor Unmanned Aerial Vehicle (UAV) has the advantages of small size, light weight, and low cost. However, imaging signal processing is complicated due to the extremely unstable flight path. Real-time adjustment of pulse repetition frequency based on inertial navigation data can compensate for the along-track displacement error in advance, but the residual error cannot be ignored for highly squinted high-band Synthetic Aperture Radar (SAR). Therefore, the residual along-track displacement error is extracted based on the difference between the measured displacement value and the ideal one, and then the Line-of-Sight (LOS) motion error of the squint imaging geometry is modified. The traditional first-order and second-order LOS error compensation factors are improved, and the tolerance of the amplitude and frequency of the sinusoidal displacement error of a multirotor UAV is analyzed based on paired echo theory. Simulation and flight experiments verify that the proposed method can reduce the LOS motion error by an order of magnitude in large squint imaging and significantly improve the imaging performance of the squinted SAR of a multirotor UAV. The multirotor Unmanned Aerial Vehicle (UAV) has the advantages of small size, light weight, and low cost. However, imaging signal processing is complicated due to the extremely unstable flight path. Real-time adjustment of pulse repetition frequency based on inertial navigation data can compensate for the along-track displacement error in advance, but the residual error cannot be ignored for highly squinted high-band Synthetic Aperture Radar (SAR). Therefore, the residual along-track displacement error is extracted based on the difference between the measured displacement value and the ideal one, and then the Line-of-Sight (LOS) motion error of the squint imaging geometry is modified. The traditional first-order and second-order LOS error compensation factors are improved, and the tolerance of the amplitude and frequency of the sinusoidal displacement error of a multirotor UAV is analyzed based on paired echo theory. Simulation and flight experiments verify that the proposed method can reduce the LOS motion error by an order of magnitude in large squint imaging and significantly improve the imaging performance of the squinted SAR of a multirotor UAV.
SAR three-dimensional (3D) imaging of complex structural facilities is an important and challenging issue in SAR imaging. The existing 3D SAR imaging relies on multiple channels or flights in the elevation direction, exerting high demands on the radar system or data acquisition. This paper proposes a 3D imaging method for complex structural facilities without a prior model, and the full-aspect 3D image of the entire scene in the region with unknown prior information can be obtained solely using a single flight. This method completely utilizes the full-aspect observation as well as the layover and elevation ambiguity resolving capabilities of circular SAR without requiring target pre-modeling and 3D imaging grid construction. Moreover, the method is suitable for fine 3D imaging complex structural facilities in large areas. Significant progress has been made in the practical technology of radar 3D imaging. The full-aspect 3D radar images of the FAST radio telescope are obtained for the first time with the proposed method, thus verifying the correctness and effectiveness of our theory and method. SAR three-dimensional (3D) imaging of complex structural facilities is an important and challenging issue in SAR imaging. The existing 3D SAR imaging relies on multiple channels or flights in the elevation direction, exerting high demands on the radar system or data acquisition. This paper proposes a 3D imaging method for complex structural facilities without a prior model, and the full-aspect 3D image of the entire scene in the region with unknown prior information can be obtained solely using a single flight. This method completely utilizes the full-aspect observation as well as the layover and elevation ambiguity resolving capabilities of circular SAR without requiring target pre-modeling and 3D imaging grid construction. Moreover, the method is suitable for fine 3D imaging complex structural facilities in large areas. Significant progress has been made in the practical technology of radar 3D imaging. The full-aspect 3D radar images of the FAST radio telescope are obtained for the first time with the proposed method, thus verifying the correctness and effectiveness of our theory and method.
In the gradually becoming information-based and intelligent modern warfare, Radar Automatic Target Recognition (RATR) technology plays an increasingly important role in military applications, such as national security defense and strategic early warning. The High-Resolution Range Profile (HRRP) reflects the distribution of target scatterers along the radar line of sight and contains a target’s rich structural information, thus being valuable for target recognition and having become a research hotspot in the field of RATR. Parametric statistical modeling aims to construct a parametric mathematical model to characterize the distribution of observed data. It is an important way to estimate the data probability distribution and mine the hidden information of data. Radar HRRP target recognition based on a parametric statistical model directly uses the estimated probability distribution for statistical recognition or inputs the extracted information hidden in data into the classifier for target recognition. The parametric statistical model exhibits advantages in prior knowledge integration, flexible expansion, parameter uncertainty evaluation, and automatic order determination combined with Bayesian theory; therefore, the overall performance of the HRRP recognition method based on such a model is better than that of other methods. Therefore, parametric statistical modeling is currently the key research direction for radar HRRP recognition. This paper summarizes the radar HRRP target recognition methods of the last 15 years from the two aspects of shallow statistical modeling and deep statistical modeling, analyzes the characteristics and problems of these methods, and forecasts the development direction of radar target recognition based on HRRP parametric statistical modeling. In the gradually becoming information-based and intelligent modern warfare, Radar Automatic Target Recognition (RATR) technology plays an increasingly important role in military applications, such as national security defense and strategic early warning. The High-Resolution Range Profile (HRRP) reflects the distribution of target scatterers along the radar line of sight and contains a target’s rich structural information, thus being valuable for target recognition and having become a research hotspot in the field of RATR. Parametric statistical modeling aims to construct a parametric mathematical model to characterize the distribution of observed data. It is an important way to estimate the data probability distribution and mine the hidden information of data. Radar HRRP target recognition based on a parametric statistical model directly uses the estimated probability distribution for statistical recognition or inputs the extracted information hidden in data into the classifier for target recognition. The parametric statistical model exhibits advantages in prior knowledge integration, flexible expansion, parameter uncertainty evaluation, and automatic order determination combined with Bayesian theory; therefore, the overall performance of the HRRP recognition method based on such a model is better than that of other methods. Therefore, parametric statistical modeling is currently the key research direction for radar HRRP recognition. This paper summarizes the radar HRRP target recognition methods of the last 15 years from the two aspects of shallow statistical modeling and deep statistical modeling, analyzes the characteristics and problems of these methods, and forecasts the development direction of radar target recognition based on HRRP parametric statistical modeling.
As a novel radar system, the Multiple-Input Multiple-Output (MIMO) radar with waveform diversity has demonstrated excellent performance in several aspects, including target detection, parameter estimation, radio frequency stealth, and anti-jamming characteristics. After nearly 20 years of in-depth research by scholars, the MIMO radar theory based on orthogonal waveforms has significantly improved. It has been widely applied in fields such as automobile-assisted driving and safety defense. In recent years, with the introduction of the concepts of electromagnetic environment perception and knowledge aid, and the application requirements of radar-active anti-jamming, radio frequency stealth, and detection-communication integration, multiple new theories and methods have been generated for the MIMO radar in system architecture, transmit waveform design, and signal processing. This paper aims to review and summarize the research works on MIMO radar published in the past 20 years, including: the principle of the orthogonal-waveform MIMO radar, its target detection performance analysis and typical applications; waveform design and characteristics of the orthogonal-waveform MIMO radar; knowledge-aided cognitive MIMO waveform design and algorithm; MIMO detection-communication integrated waveform design and algorithm; MIMO radar parameter estimation; MIMO radar target detection; and MIMO radar resource management and scheduling. Finally, the paper discusses the clutter suppression and Space-Time Adaptive Processing (STAP) of MIMO radar in airborne applications, the signal processing of MIMO radar in imaging, and the signal processing of chirp millimeter-wave (mmWave) MIMO radar based on time division multi-waveform diversity. As a novel radar system, the Multiple-Input Multiple-Output (MIMO) radar with waveform diversity has demonstrated excellent performance in several aspects, including target detection, parameter estimation, radio frequency stealth, and anti-jamming characteristics. After nearly 20 years of in-depth research by scholars, the MIMO radar theory based on orthogonal waveforms has significantly improved. It has been widely applied in fields such as automobile-assisted driving and safety defense. In recent years, with the introduction of the concepts of electromagnetic environment perception and knowledge aid, and the application requirements of radar-active anti-jamming, radio frequency stealth, and detection-communication integration, multiple new theories and methods have been generated for the MIMO radar in system architecture, transmit waveform design, and signal processing. This paper aims to review and summarize the research works on MIMO radar published in the past 20 years, including: the principle of the orthogonal-waveform MIMO radar, its target detection performance analysis and typical applications; waveform design and characteristics of the orthogonal-waveform MIMO radar; knowledge-aided cognitive MIMO waveform design and algorithm; MIMO detection-communication integrated waveform design and algorithm; MIMO radar parameter estimation; MIMO radar target detection; and MIMO radar resource management and scheduling. Finally, the paper discusses the clutter suppression and Space-Time Adaptive Processing (STAP) of MIMO radar in airborne applications, the signal processing of MIMO radar in imaging, and the signal processing of chirp millimeter-wave (mmWave) MIMO radar based on time division multi-waveform diversity.
To reduce the probability of UAV (Unmanned Aerial Vehicle) being destroyed during a reconnaissance mission, this study proposes an effective path planning algorithm to reduce the target threat. First, high-resolution airborne radar is used for robust tracking and estimation of multiple extended targets. Subsequently, the targets are classified based on the threat degree calculated via fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). Next, path planning of a UAV is performed considering joint optimization of multiple task decision-making (the joint evaluation of the target threat degree and target tracking performance) as an evaluation criterion. The simulation results indicate that the fuzzy threat assessment method is effective in multiple extended target tracking, and the proposed UAV path planning algorithm is reasonable. Thus the target threat is efficiently reduced without losing the tracking accuracy. To reduce the probability of UAV (Unmanned Aerial Vehicle) being destroyed during a reconnaissance mission, this study proposes an effective path planning algorithm to reduce the target threat. First, high-resolution airborne radar is used for robust tracking and estimation of multiple extended targets. Subsequently, the targets are classified based on the threat degree calculated via fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). Next, path planning of a UAV is performed considering joint optimization of multiple task decision-making (the joint evaluation of the target threat degree and target tracking performance) as an evaluation criterion. The simulation results indicate that the fuzzy threat assessment method is effective in multiple extended target tracking, and the proposed UAV path planning algorithm is reasonable. Thus the target threat is efficiently reduced without losing the tracking accuracy.
The traditional coherent radar signal processing generally adopts the cascaded processing method of pulse compression and Radon-Fourier Transform (RFT) for a target moving across the range cell. However, the cascaded processing exhibits the following problems: first, during the energy integration of a high-speed target, problems including the offset of the target peak, even broadening of the main lobe, gain reduction and increases in the side lobes will occur; second, the lack of effective clutter suppression affects the detection of weak targets. Based on the multi-dimensional signal combination and clutter suppression, this paper proposes a Time-Range Focus-Before-Detect method (Adaptive-Pulse Compression Radon-Fourier Transform, A-PCRFT) in clutter background, which combines the pulse compression, RFT and adaptive clutter suppression. First, the method combines the two radar signal processing dimensions of intra-pulse time (fast time) and inter-pulse time (slow time). The two-dimensional steering vector corresponding to the high-speed target is introduced to compensate for the intra-pulse time and inter-pulse Doppler shifts; Then, the clutter covariance matrix before pulse compression is estimated based on the secondary data; Finally, the optimal filter weight vector is determined according to the clutter covariance matrix and the steering vector. This method can effectively suppress the clutter and focus the target energy simultaneously in the range-velocity space. Simulation results show that this method is superior to the cascaded method, which adopts the pulse compression and adaptive Radon–Fourier transform. The traditional coherent radar signal processing generally adopts the cascaded processing method of pulse compression and Radon-Fourier Transform (RFT) for a target moving across the range cell. However, the cascaded processing exhibits the following problems: first, during the energy integration of a high-speed target, problems including the offset of the target peak, even broadening of the main lobe, gain reduction and increases in the side lobes will occur; second, the lack of effective clutter suppression affects the detection of weak targets. Based on the multi-dimensional signal combination and clutter suppression, this paper proposes a Time-Range Focus-Before-Detect method (Adaptive-Pulse Compression Radon-Fourier Transform, A-PCRFT) in clutter background, which combines the pulse compression, RFT and adaptive clutter suppression. First, the method combines the two radar signal processing dimensions of intra-pulse time (fast time) and inter-pulse time (slow time). The two-dimensional steering vector corresponding to the high-speed target is introduced to compensate for the intra-pulse time and inter-pulse Doppler shifts; Then, the clutter covariance matrix before pulse compression is estimated based on the secondary data; Finally, the optimal filter weight vector is determined according to the clutter covariance matrix and the steering vector. This method can effectively suppress the clutter and focus the target energy simultaneously in the range-velocity space. Simulation results show that this method is superior to the cascaded method, which adopts the pulse compression and adaptive Radon–Fourier transform.
Synthetic Aperture Radar (SAR) image registration has recently been one of the most challenging tasks because of speckle noise, geometric distortion and nonlinear radiation differences between SAR images. The repeatability of keypoints and the effectiveness of feature descriptors directly affect the registration accuracy of feature-based methods. In this paper, we propose a novel Feature Intersection-based (FI) keypoint detector, which contains three parallel detectors, i.e., a Phase Congruency (PC) detector, horizontal/vertical oriented gradient detectors, and a Local Coefficient of Variation (LCoV) detector. The proposed FI detector can effectively extract keypoints with high repeatabilityand greatly reduce the number of false keypoints, thus greatly reducing the computational cost of feature description and matching. We further propose the Siamese Cross Stage Partial Network (Sim-CSPNet) to rapidly extract feature descriptors containing deep and shallow features, which can obtain more correct matching point pairs than traditional synthetic shallow descriptors. Through the registration experiments on multiple sets of SAR images, the proposed method is verified to have better registration results than the three existing methods. Synthetic Aperture Radar (SAR) image registration has recently been one of the most challenging tasks because of speckle noise, geometric distortion and nonlinear radiation differences between SAR images. The repeatability of keypoints and the effectiveness of feature descriptors directly affect the registration accuracy of feature-based methods. In this paper, we propose a novel Feature Intersection-based (FI) keypoint detector, which contains three parallel detectors, i.e., a Phase Congruency (PC) detector, horizontal/vertical oriented gradient detectors, and a Local Coefficient of Variation (LCoV) detector. The proposed FI detector can effectively extract keypoints with high repeatabilityand greatly reduce the number of false keypoints, thus greatly reducing the computational cost of feature description and matching. We further propose the Siamese Cross Stage Partial Network (Sim-CSPNet) to rapidly extract feature descriptors containing deep and shallow features, which can obtain more correct matching point pairs than traditional synthetic shallow descriptors. Through the registration experiments on multiple sets of SAR images, the proposed method is verified to have better registration results than the three existing methods.
Atmospheric influence is the main interference factor in Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) deformation monitoring. Due to the complex terrain and various environmental factors, the correction method based on the assumption of a uniform atmospheric influence may lead to low atmospheric correction accuracy. In this paper, a two-stage semi-empirical model is proposed to correct the atmospheric phase screen during the GB-InSAR monitoring of a super large slope under complex atmospheric conditions. First, the observed atmospheric phase is modeled according to the height and range of the terrain structure to correct the linear atmospheric phase. Then, considering the complex atmospheric conditions and the spatially nonuniform atmosphere with a large azimuth field of view, stable Persistent Scatterers (PS) are selected to obtain the atmospheric phase of all PS by interpolation to correct the nonlinear atmospheric phase. This method is used to process a large field of view radar image of the foundation of the Xinpu and Outang landslides in the Three Gorges Reservoir area. Compared with the conventional method, the atmospheric phase error is reduced by approximately 2 mm. This method effectively corrects the nonuniform atmospheric phase under the landslide monitoring scene and meets the wide-area monitoring needs of the landslide. Atmospheric influence is the main interference factor in Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) deformation monitoring. Due to the complex terrain and various environmental factors, the correction method based on the assumption of a uniform atmospheric influence may lead to low atmospheric correction accuracy. In this paper, a two-stage semi-empirical model is proposed to correct the atmospheric phase screen during the GB-InSAR monitoring of a super large slope under complex atmospheric conditions. First, the observed atmospheric phase is modeled according to the height and range of the terrain structure to correct the linear atmospheric phase. Then, considering the complex atmospheric conditions and the spatially nonuniform atmosphere with a large azimuth field of view, stable Persistent Scatterers (PS) are selected to obtain the atmospheric phase of all PS by interpolation to correct the nonlinear atmospheric phase. This method is used to process a large field of view radar image of the foundation of the Xinpu and Outang landslides in the Three Gorges Reservoir area. Compared with the conventional method, the atmospheric phase error is reduced by approximately 2 mm. This method effectively corrects the nonuniform atmospheric phase under the landslide monitoring scene and meets the wide-area monitoring needs of the landslide.
The clutter of space-based early warning radar exhibits tight coupling in the azimuth-elevation-Doppler domain due to the high speed of satellites and the Earth’s rotation. As a result, conventional Space-Time Adaptive Processing (STAP) suffers significant performance degradation when detecting slow moving targets. The azimuth-elevation-Doppler three-dimensional STAP method provides the ability to decouple clutter and thus can achieve sub-optimal performance for clutter suppression. However, in contrast to the situation in non-sidelooking airborne early warning radar, this method requires large system degrees of freedom when applied to space-based early warning radar. Therefore, in practice, both the computational load and the sample requirement are too large to meet. In this study, the space-time signal model of the planar array for space-based early warning radar is first constructed. Then, the tight coupling characteristic of clutter in the azimuth-elevation-Doppler domain is analyzed in detail. On this basis, a novel three-dimensional STAP method with reduced degrees of freedom with factored structure is proposed. The sidelobe clutter is first suppressed via amplitude taper in azimuth, and the mainlobe clutter responding to each ambiguous range is further canceled by adaptive processing in the elevation-Doppler domain. The simulation results show that the proposed method can achieve sub-optimal performance under low computational load and limited sample conditions. Therefore, the proposed method is suitable for practical application in space-based early warning radar. The clutter of space-based early warning radar exhibits tight coupling in the azimuth-elevation-Doppler domain due to the high speed of satellites and the Earth’s rotation. As a result, conventional Space-Time Adaptive Processing (STAP) suffers significant performance degradation when detecting slow moving targets. The azimuth-elevation-Doppler three-dimensional STAP method provides the ability to decouple clutter and thus can achieve sub-optimal performance for clutter suppression. However, in contrast to the situation in non-sidelooking airborne early warning radar, this method requires large system degrees of freedom when applied to space-based early warning radar. Therefore, in practice, both the computational load and the sample requirement are too large to meet. In this study, the space-time signal model of the planar array for space-based early warning radar is first constructed. Then, the tight coupling characteristic of clutter in the azimuth-elevation-Doppler domain is analyzed in detail. On this basis, a novel three-dimensional STAP method with reduced degrees of freedom with factored structure is proposed. The sidelobe clutter is first suppressed via amplitude taper in azimuth, and the mainlobe clutter responding to each ambiguous range is further canceled by adaptive processing in the elevation-Doppler domain. The simulation results show that the proposed method can achieve sub-optimal performance under low computational load and limited sample conditions. Therefore, the proposed method is suitable for practical application in space-based early warning radar.
The interrupted-sampling repeater jammer can sample, store, process and transmit part of the radar transmitter signal multiple times and the fake targets will form on the radar receiver. To improve the radar performance in the aforementioned jamming scenario, in this study, a new signal differential feature extraction method is proposed, and a judgment criterion is formulated based on the difference between the target echo and the Interrupted-Sampling Repeater Jamming (ISRJ) in the differential feature space to effectively identify and suppress the ISRJ while achieving target detection. Simulation results show that the proposed method has a remarkable ISRJ suppression performance. The equivalent signal-to-noise ratio is improved by at least 4.2 dB compared with three typical time-frequency domain filtering algorithms. The interrupted-sampling repeater jammer can sample, store, process and transmit part of the radar transmitter signal multiple times and the fake targets will form on the radar receiver. To improve the radar performance in the aforementioned jamming scenario, in this study, a new signal differential feature extraction method is proposed, and a judgment criterion is formulated based on the difference between the target echo and the Interrupted-Sampling Repeater Jamming (ISRJ) in the differential feature space to effectively identify and suppress the ISRJ while achieving target detection. Simulation results show that the proposed method has a remarkable ISRJ suppression performance. The equivalent signal-to-noise ratio is improved by at least 4.2 dB compared with three typical time-frequency domain filtering algorithms.
Synthetic Aperture Radar (SAR) image ship target detection has attracted considerable attention. As a state-of-the-art method, the Constant False Alarm Rate (CFAR) detection algorithm is often used in SAR image ship target detection. However, the detection performance of the classical CFAR is easily affected by speckle noise. Moreover, the detection results based on the sliding window are sensitive to the size of the sliding window. Thus, ensuring that there are no target pixels in the cluttered background is difficult, which easily leads to a high computational load. This study proposes a new ship target detection method for SAR images based on fast superpixel-based non-window CFAR to solve these problems. The superpixel generation method of Density-Based Spatial Clustering of Applications with Noise is used to generate superpixels for SAR images. Under the assumption that SAR data obey the Rayleigh mixture distribution, we define a superpixel dissimilarity measure. Then, the clutter parameters of each pixel are accurately estimated using superpixels, which can avoid the shortcomings of the traditional CFAR sliding window even in the case of multiple targets. A local contrast based on the Coefficient of Variation (CoV) of the SAR image is proposed to optimize the CFAR detection result, which can eliminate a large number of false alarms from man-made targets in urban areas. The experimental results of five real SAR images show that the proposed method for ship target detection in SAR images with different scenes is robust compared with other state-of-the-art methods. Synthetic Aperture Radar (SAR) image ship target detection has attracted considerable attention. As a state-of-the-art method, the Constant False Alarm Rate (CFAR) detection algorithm is often used in SAR image ship target detection. However, the detection performance of the classical CFAR is easily affected by speckle noise. Moreover, the detection results based on the sliding window are sensitive to the size of the sliding window. Thus, ensuring that there are no target pixels in the cluttered background is difficult, which easily leads to a high computational load. This study proposes a new ship target detection method for SAR images based on fast superpixel-based non-window CFAR to solve these problems. The superpixel generation method of Density-Based Spatial Clustering of Applications with Noise is used to generate superpixels for SAR images. Under the assumption that SAR data obey the Rayleigh mixture distribution, we define a superpixel dissimilarity measure. Then, the clutter parameters of each pixel are accurately estimated using superpixels, which can avoid the shortcomings of the traditional CFAR sliding window even in the case of multiple targets. A local contrast based on the Coefficient of Variation (CoV) of the SAR image is proposed to optimize the CFAR detection result, which can eliminate a large number of false alarms from man-made targets in urban areas. The experimental results of five real SAR images show that the proposed method for ship target detection in SAR images with different scenes is robust compared with other state-of-the-art methods.
This study proposes a multi-rank range-spread target detection method for multi-channel array radar under a non-Gaussian clutter background. The method aims to detect the target from real clutter using the multi-channel array radar. First, a multi-rank range-spread target model was formulated using a subspace matrix with a rank greater than one and the coordinate vectors of corresponding range bins. Then, by exploiting the persymmetric structure information of the clutter covariance matrix under the detection scenario, wherein the radar receiver units were central symmetric in space or time, a small sample estimation strategy for the parameters to be solved through the unitary transformation was constructed. Further, a non-Gaussian clutter background multi-rank range-spread target detection method was designed based on the generalized likelihood ratio, Rao, and Wald tests. Finally, a theoretical derivation proved that the proposed detection method has the constant false alarm rate property. The experimental results based on both the simulated and measured data showed that the proposed detection method can ensure the constant false alarm rate property of the clutter covariance matrix. Additionally, compared with the existing detection methods, the proposed detection method improves the target detection performance under small sample support. Besides, the proposed detection method effectively improves the robustness of target detection under the condition of steering vector mismatch. This study proposes a multi-rank range-spread target detection method for multi-channel array radar under a non-Gaussian clutter background. The method aims to detect the target from real clutter using the multi-channel array radar. First, a multi-rank range-spread target model was formulated using a subspace matrix with a rank greater than one and the coordinate vectors of corresponding range bins. Then, by exploiting the persymmetric structure information of the clutter covariance matrix under the detection scenario, wherein the radar receiver units were central symmetric in space or time, a small sample estimation strategy for the parameters to be solved through the unitary transformation was constructed. Further, a non-Gaussian clutter background multi-rank range-spread target detection method was designed based on the generalized likelihood ratio, Rao, and Wald tests. Finally, a theoretical derivation proved that the proposed detection method has the constant false alarm rate property. The experimental results based on both the simulated and measured data showed that the proposed detection method can ensure the constant false alarm rate property of the clutter covariance matrix. Additionally, compared with the existing detection methods, the proposed detection method improves the target detection performance under small sample support. Besides, the proposed detection method effectively improves the robustness of target detection under the condition of steering vector mismatch.
In the Synthetic Aperture Radar (SAR) ship target detection task, the targets have a large aspect ratio and dense distribution, and they are arranged in arbitrary directions. The oriented bounding box-based detection methods can output accurate detection results. However, these methods are strongly restricted by high computational complexity, slow inference speed, and large storage consumption, which complicate their deployment on space-borne platforms. To solve the above issues, a lightweight oriented anchor-free-based detection method is proposed by combining feature map and prediction head knowledge distillation. First, we propose an improved Gaussian kernel based on the aspect ratio and angle information so that the generated heatmaps can better describe the shape of the targets. Second, the foreground region enhancement branch is introduced to make the network focus more on foreground features while suppressing the background interference. When training the lightweight student network, the similarity between pixels is treated as transferred knowledge in heatmap distillation. To tackle the imbalance between positive and negative samples in feature distillation, the foreground attention region is applied as a mask to guide the feature distillation process. In addition, a global semantic module is proposed to model the contextual information around pixels, and the background knowledge is combined to further strengthen the feature representation. Experimental results based on HRSID show that our method can achieve 80.71% mAP with only 9.07 M model parameters, and the detection frame rate meets the needs of real-time applications. In the Synthetic Aperture Radar (SAR) ship target detection task, the targets have a large aspect ratio and dense distribution, and they are arranged in arbitrary directions. The oriented bounding box-based detection methods can output accurate detection results. However, these methods are strongly restricted by high computational complexity, slow inference speed, and large storage consumption, which complicate their deployment on space-borne platforms. To solve the above issues, a lightweight oriented anchor-free-based detection method is proposed by combining feature map and prediction head knowledge distillation. First, we propose an improved Gaussian kernel based on the aspect ratio and angle information so that the generated heatmaps can better describe the shape of the targets. Second, the foreground region enhancement branch is introduced to make the network focus more on foreground features while suppressing the background interference. When training the lightweight student network, the similarity between pixels is treated as transferred knowledge in heatmap distillation. To tackle the imbalance between positive and negative samples in feature distillation, the foreground attention region is applied as a mask to guide the feature distillation process. In addition, a global semantic module is proposed to model the contextual information around pixels, and the background knowledge is combined to further strengthen the feature representation. Experimental results based on HRSID show that our method can achieve 80.71% mAP with only 9.07 M model parameters, and the detection frame rate meets the needs of real-time applications.
This paper studies adaptive distributed targets detection for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar, where the targets are embedded in Gaussian clutter with unknown covariance matrix. The proposed FDA-MIMO radar detection model considers also the distributed targets establishing as a summation expression, which is different from the classic detection models in MIMO and/or phase array radars that discuss only point-like targets. Next, the detector through Rao criterion without the need of training data are proposed. The proposed method together with all theoretical analysis are verified by numerical results. This paper studies adaptive distributed targets detection for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar, where the targets are embedded in Gaussian clutter with unknown covariance matrix. The proposed FDA-MIMO radar detection model considers also the distributed targets establishing as a summation expression, which is different from the classic detection models in MIMO and/or phase array radars that discuss only point-like targets. Next, the detector through Rao criterion without the need of training data are proposed. The proposed method together with all theoretical analysis are verified by numerical results.
Compared with optical images, the background clutter has a greater impact on feature extraction in Synthetic Aperture Radar (SAR) images. Due to the traditional redundant region proposals on the entire feature map, these algorithms generate large quantities of false alarms under the influence of clutter in SAR images, thereby lowering the target detection accuracy. To address this issue, this study proposes a Faster R-CNN model-based SAR target detection method, which uses reinforcement learning to realize adaptive region proposal selection. This method can adaptively locate areas that may contain targets on the feature map using the sequential decision-making characteristic of reinforcement learning and simultaneously adjust the scope of the next search area according to previous search results using distance constraints in reinforcement learning. Thus, this method can reduce the impact of complex background clutter and the computation of reinforcement learning. The experimental results based on the measured data indicate that the proposed method improves the detection performance. Compared with optical images, the background clutter has a greater impact on feature extraction in Synthetic Aperture Radar (SAR) images. Due to the traditional redundant region proposals on the entire feature map, these algorithms generate large quantities of false alarms under the influence of clutter in SAR images, thereby lowering the target detection accuracy. To address this issue, this study proposes a Faster R-CNN model-based SAR target detection method, which uses reinforcement learning to realize adaptive region proposal selection. This method can adaptively locate areas that may contain targets on the feature map using the sequential decision-making characteristic of reinforcement learning and simultaneously adjust the scope of the next search area according to previous search results using distance constraints in reinforcement learning. Thus, this method can reduce the impact of complex background clutter and the computation of reinforcement learning. The experimental results based on the measured data indicate that the proposed method improves the detection performance.
Dense-repeated jamming is highly related to the radar-transmitted signal, and it has suppression and deception jamming effects, which makes detecting the real target difficult for a radar system and seriously threatens the operational capability of radar. To solve this problem, an intelligent suppression method based on the Support Vector Machine (SVM) is proposed in this paper. The optimal SVM model is obtained through offline training on a random sample set to intelligently identify and classify targets and interference. Then, the interference sidelobe in the target range unit is further suppressed by smoothing filtering. Finally, high-resolution two-dimensional reconstruction is performed based on compress sensing theory to estimate the target parameter information. Simulation experiments and measured data processing results reveal that the proposed algorithm can effectively suppress dense-repeated jamming and accurately detect real targets in different scenarios. Dense-repeated jamming is highly related to the radar-transmitted signal, and it has suppression and deception jamming effects, which makes detecting the real target difficult for a radar system and seriously threatens the operational capability of radar. To solve this problem, an intelligent suppression method based on the Support Vector Machine (SVM) is proposed in this paper. The optimal SVM model is obtained through offline training on a random sample set to intelligently identify and classify targets and interference. Then, the interference sidelobe in the target range unit is further suppressed by smoothing filtering. Finally, high-resolution two-dimensional reconstruction is performed based on compress sensing theory to estimate the target parameter information. Simulation experiments and measured data processing results reveal that the proposed algorithm can effectively suppress dense-repeated jamming and accurately detect real targets in different scenarios.
This paper investigates the joint optimization problem of transmit resources and trajectory planning for target tracking in airborne radar networks. First, the analytical expression for the Bayesian Cramér-Rao Lower Bound (BCRLB) with the variables of the radar transmit power, dwell time, transmit signal Gaussian pulse length and signal bandwidth, and speed and heading angle of airborne nodes is derived and adopted as the metric function to evaluate the target tracking accuracy. In addition, the analytical expression of intercept probability with the variables of the radar transmit power, dwell time, and speed and heading angle of airborne nodes is also derived and utilized as the metric function to gauge the radio frequency stealth performance of the overall system. On this basis, a joint optimization model of transmit resources and trajectory planning for target tracking in airborne radar networks is established to jointly optimize the radar transmit power, dwell time, transmit signal Gaussian pulse length and signal bandwidth, and speed and heading angle of airborne nodes. This is done to minimize the target estimation error BCRLB under the constraints of given system resources, aircraft maneuvering and intercept probability threshold, thereby improving the target tracking accuracy of airborne radar network. Subsequently, a five-step decomposition iterative algorithm incorporating the particle swarm algorithm is used to solve the underlying optimization problem. The simulation results demonstrate that the target tracking accuracy of the proposed algorithm outperforms other existing approaches. This paper investigates the joint optimization problem of transmit resources and trajectory planning for target tracking in airborne radar networks. First, the analytical expression for the Bayesian Cramér-Rao Lower Bound (BCRLB) with the variables of the radar transmit power, dwell time, transmit signal Gaussian pulse length and signal bandwidth, and speed and heading angle of airborne nodes is derived and adopted as the metric function to evaluate the target tracking accuracy. In addition, the analytical expression of intercept probability with the variables of the radar transmit power, dwell time, and speed and heading angle of airborne nodes is also derived and utilized as the metric function to gauge the radio frequency stealth performance of the overall system. On this basis, a joint optimization model of transmit resources and trajectory planning for target tracking in airborne radar networks is established to jointly optimize the radar transmit power, dwell time, transmit signal Gaussian pulse length and signal bandwidth, and speed and heading angle of airborne nodes. This is done to minimize the target estimation error BCRLB under the constraints of given system resources, aircraft maneuvering and intercept probability threshold, thereby improving the target tracking accuracy of airborne radar network. Subsequently, a five-step decomposition iterative algorithm incorporating the particle swarm algorithm is used to solve the underlying optimization problem. The simulation results demonstrate that the target tracking accuracy of the proposed algorithm outperforms other existing approaches.
Due to several advantages of the Multi-Input Multi-Output (MIMO) system in terms of waveform, space diversity, and multiplexing, the MIMO Dual Function Radar and Communication (DFRC) system, which is responsible for target detection and securing the communication by sharing the software and hardware resources, has attracted great attention. This paper addresses the MIMO DFRC system based on permutation matrix modulation and proposes a DFRC signal matrix design method based on the Alternation Direction Method of Multipliers (ADMM). By maximizing the Peak Mainlobe to Sidelobe level Ratio (PMSR) of the beampattern with the constraints of the reference codebook for both users and eavesdroppers, the system guarantees excellent detection performance along with protecting the communication information from interception. Aiming at the communication demodulation of the permutation matrix, a permutation learning demodulation method based on the Alternating Direction Penalty Method (ADPM) is proposed to improve the demodulation efficiency of the co-use waveform. Numerical simulations verify the effectiveness of the proposed methods to achieve dual function, capable of realizing multiuser communication and deriving higher PMSR compared with the existing counterparts. Due to several advantages of the Multi-Input Multi-Output (MIMO) system in terms of waveform, space diversity, and multiplexing, the MIMO Dual Function Radar and Communication (DFRC) system, which is responsible for target detection and securing the communication by sharing the software and hardware resources, has attracted great attention. This paper addresses the MIMO DFRC system based on permutation matrix modulation and proposes a DFRC signal matrix design method based on the Alternation Direction Method of Multipliers (ADMM). By maximizing the Peak Mainlobe to Sidelobe level Ratio (PMSR) of the beampattern with the constraints of the reference codebook for both users and eavesdroppers, the system guarantees excellent detection performance along with protecting the communication information from interception. Aiming at the communication demodulation of the permutation matrix, a permutation learning demodulation method based on the Alternating Direction Penalty Method (ADPM) is proposed to improve the demodulation efficiency of the co-use waveform. Numerical simulations verify the effectiveness of the proposed methods to achieve dual function, capable of realizing multiuser communication and deriving higher PMSR compared with the existing counterparts.
For the resource allocation problem of multitarget tracking in a spectral coexistence environment, this study proposes a joint transmit power and dwell time allocation algorithm for radar networks. First, the predicted Bayesian Cramér-Rao Lower Bound (BCRLB) with the variables of radar node selection, transmit power and dwell time is derived as the performance metric for multi-target tracking accuracy. On this basis, a joint optimization model of transmit power and dwell time allocation for multitarget tracking in radar networks under spectral coexistence is built to collaboratively optimize the radar node selection, transmit power and dwell time of radar networks, This joint optimization model aims to minimize the multitarget tracking BCRLB while satisfying the given transmit resources of radar networks and the predetermined maximum allowable interference energy threshold of the communication base station. Subsequently, for the aforementioned optimization problem, a two-step decomposition method is used to decompose it into multiple subconvex problems, which are solved by combining the Semi-Definite Programming (SDP) and cyclic minimization algorithms. The simulation results showed that, compared with the existing algorithms, the proposed algorithm can effectively improve the multitarget tracking accuracy of radar networks while ensuring that the communication base station works properly. For the resource allocation problem of multitarget tracking in a spectral coexistence environment, this study proposes a joint transmit power and dwell time allocation algorithm for radar networks. First, the predicted Bayesian Cramér-Rao Lower Bound (BCRLB) with the variables of radar node selection, transmit power and dwell time is derived as the performance metric for multi-target tracking accuracy. On this basis, a joint optimization model of transmit power and dwell time allocation for multitarget tracking in radar networks under spectral coexistence is built to collaboratively optimize the radar node selection, transmit power and dwell time of radar networks, This joint optimization model aims to minimize the multitarget tracking BCRLB while satisfying the given transmit resources of radar networks and the predetermined maximum allowable interference energy threshold of the communication base station. Subsequently, for the aforementioned optimization problem, a two-step decomposition method is used to decompose it into multiple subconvex problems, which are solved by combining the Semi-Definite Programming (SDP) and cyclic minimization algorithms. The simulation results showed that, compared with the existing algorithms, the proposed algorithm can effectively improve the multitarget tracking accuracy of radar networks while ensuring that the communication base station works properly.
Multi-sensor multi-target tracking is a popular topic in the field of information fusion. It improves the accuracy and stability of target tracking by fusing multiple local sensor information. By the fusion system, the multi-sensor multi-target tracking is grouped into distributed fusion, centralized fusion, and hybrid fusion. Distributed fusion is widely applied in the military and civilian fields with the advantages of strong reliability, high stability, and low requirements on network communication bandwidth. Key techniques of distributed multi-sensor multi-target tracking include multi-target tracking, sensor registration, track-to-track association, and data fusion. This paper reviews the theoretical basis and applicable conditions of these key techniques, highlights the incomplete measurement spatial registration algorithm and track association algorithm, and provides the simulation results. Finally, the weaknesses of the key techniques of distributed multi-sensor multi-target tracking are summarized, and the future development trends of these key techniques are surveyed. Multi-sensor multi-target tracking is a popular topic in the field of information fusion. It improves the accuracy and stability of target tracking by fusing multiple local sensor information. By the fusion system, the multi-sensor multi-target tracking is grouped into distributed fusion, centralized fusion, and hybrid fusion. Distributed fusion is widely applied in the military and civilian fields with the advantages of strong reliability, high stability, and low requirements on network communication bandwidth. Key techniques of distributed multi-sensor multi-target tracking include multi-target tracking, sensor registration, track-to-track association, and data fusion. This paper reviews the theoretical basis and applicable conditions of these key techniques, highlights the incomplete measurement spatial registration algorithm and track association algorithm, and provides the simulation results. Finally, the weaknesses of the key techniques of distributed multi-sensor multi-target tracking are summarized, and the future development trends of these key techniques are surveyed.
The development of 3D Synthetic Aperture Radar (SAR) imaging is currently hampered by issues such as high data dimension, high system complexity, and low imaging processing efficiency. Sparse SAR imaging has grown in importance as a research branch in SAR imaging due to the high potential of sparse signal processing techniques based on Compressed Sensing (CS) to show high potential in reducing system complexity and improving imaging quality. However, traditional sparse imaging methods are still constrained by high computational complexity, nontrivial parameter tuning, and poor adaptability to weakly sparse scenes. To address these issues, we propose a new 3D SAR imaging method based on learned sparse priors inspired by the deep unfolding concept. First, the limitations of the matrix-vector linear representation model are discussed, and an imaging operator is introduced to improve the algorithm's imaging efficiency. Furthermore, this research focuses on algorithm network details, such as network topology design, the problem of complex-valued propagations, optimization constraints of algorithm parameters, and network training details. Finally, through simulations and measured experiments, it is proved that the proposed method can improve the imaging accuracy while reducing the running time by more than one order of magnitude compared with the conventional sparse imaging algorithms. The development of 3D Synthetic Aperture Radar (SAR) imaging is currently hampered by issues such as high data dimension, high system complexity, and low imaging processing efficiency. Sparse SAR imaging has grown in importance as a research branch in SAR imaging due to the high potential of sparse signal processing techniques based on Compressed Sensing (CS) to show high potential in reducing system complexity and improving imaging quality. However, traditional sparse imaging methods are still constrained by high computational complexity, nontrivial parameter tuning, and poor adaptability to weakly sparse scenes. To address these issues, we propose a new 3D SAR imaging method based on learned sparse priors inspired by the deep unfolding concept. First, the limitations of the matrix-vector linear representation model are discussed, and an imaging operator is introduced to improve the algorithm's imaging efficiency. Furthermore, this research focuses on algorithm network details, such as network topology design, the problem of complex-valued propagations, optimization constraints of algorithm parameters, and network training details. Finally, through simulations and measured experiments, it is proved that the proposed method can improve the imaging accuracy while reducing the running time by more than one order of magnitude compared with the conventional sparse imaging algorithms.
Cyclic prefixes in joint radar and communication systems based on Orthogonal Frequency Division Multiplexing (OFDM) and low probability of interception lead to weak radar echo masking on the battlefield. To address this problem, a low probability of interception waveform design scheme based on Filter Bank Multi-Carrier (FBMC) with Offset Quadrature Amplitude Modulation (FBMC-OQAM) is proposed in this paper. Mathematical models for the FBMC joint radar and communication waveform, target detection probability, and communication channel capacity are established. Under the radar and communication performance constraints required by the system, a joint optimization problem of minimizing the total transmitted power of the system is designed, and the subcarrier and power allocation scheme are optimized. Furthermore, the proposed algorithm can realize adaptive transmission where the parameters of the transmitting waveform can be optimally designed for the next pulse by utilizing the measured values of the current signal and the channel state information. Moreover, the feasibility and advantages of FBMC as the radar signal are analyzed based on the average ambiguity function. Theoretical analysis and simulation experiments show that the power allocation scheme proposed in this paper can effectively reduce the total transmitted power of the system, to achieve low interception performance compared with the equal power allocation. The FBMC waveform can effectively reduce the sidelobes caused by cyclic prefixes, which improves the radar resolution and information rate. Cyclic prefixes in joint radar and communication systems based on Orthogonal Frequency Division Multiplexing (OFDM) and low probability of interception lead to weak radar echo masking on the battlefield. To address this problem, a low probability of interception waveform design scheme based on Filter Bank Multi-Carrier (FBMC) with Offset Quadrature Amplitude Modulation (FBMC-OQAM) is proposed in this paper. Mathematical models for the FBMC joint radar and communication waveform, target detection probability, and communication channel capacity are established. Under the radar and communication performance constraints required by the system, a joint optimization problem of minimizing the total transmitted power of the system is designed, and the subcarrier and power allocation scheme are optimized. Furthermore, the proposed algorithm can realize adaptive transmission where the parameters of the transmitting waveform can be optimally designed for the next pulse by utilizing the measured values of the current signal and the channel state information. Moreover, the feasibility and advantages of FBMC as the radar signal are analyzed based on the average ambiguity function. Theoretical analysis and simulation experiments show that the power allocation scheme proposed in this paper can effectively reduce the total transmitted power of the system, to achieve low interception performance compared with the equal power allocation. The FBMC waveform can effectively reduce the sidelobes caused by cyclic prefixes, which improves the radar resolution and information rate.