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Tomographic Synthetic Aperture Radar (TomoSAR) is an advanced technology for three-dimensional (3D) mountain reconstruction. However, the TomoSAR mountain point clouds have a significant location error in the elevation direction, making high-precision 3D reconstruction of mountains difficult. A geometry constrained Moving Least Squares (MLS)-based high-precision 3D reconstruction method is addressed in this issue. This method not only has the benefits of the traditional MLS in that it uses the local subspace principle for fitting complex surface structures but also fully uses the TomoSAR point cloud characteristic of monotonically increasing elevation with ground distance for reconstruction error correction. The point clouds are first projected onto a new azimuth-ground-elevation domain. Subsequently, the suggested iterative solution-based geometry constrained MLS performs location error correction in the elevation direction. Finally, the projection transformation is used to generate 3D reconstruction results of mountains. The simulation and measurement of airborne array TomoSAR mountain data, AW3D30 DSM data, and 1:10,000 DEM data validate the effectiveness of the proposed method and demonstrate the feasibility and superiority of airborne array TomoSAR for applications such as high-precision 3D mountain reconstruction. Tomographic Synthetic Aperture Radar (TomoSAR) is an advanced technology for three-dimensional (3D) mountain reconstruction. However, the TomoSAR mountain point clouds have a significant location error in the elevation direction, making high-precision 3D reconstruction of mountains difficult. A geometry constrained Moving Least Squares (MLS)-based high-precision 3D reconstruction method is addressed in this issue. This method not only has the benefits of the traditional MLS in that it uses the local subspace principle for fitting complex surface structures but also fully uses the TomoSAR point cloud characteristic of monotonically increasing elevation with ground distance for reconstruction error correction. The point clouds are first projected onto a new azimuth-ground-elevation domain. Subsequently, the suggested iterative solution-based geometry constrained MLS performs location error correction in the elevation direction. Finally, the projection transformation is used to generate 3D reconstruction results of mountains. The simulation and measurement of airborne array TomoSAR mountain data, AW3D30 DSM data, and 1:10,000 DEM data validate the effectiveness of the proposed method and demonstrate the feasibility and superiority of airborne array TomoSAR for applications such as high-precision 3D mountain reconstruction.
This paper proposes an SEI method based on cost-sensitive learning and semisupervised generative adversarial networks to address the problem of incomplete sample labels and imbalanced data category distribution in Specific Emitter Identification (SEI), which leads to a decline in inaccuracy. Through semisupervised training, the method optimizes the network parameters of the generator and discriminator, adds a multiscale topological block to ResNet to fuse the multi-dimensional resolution features of the time-domain signal, and attributes additional labels to the generated samples to directly use the discriminator to complete the classification. Simultaneously, a cost-sensitive loss is designed to alleviate the imbalance of gradient propagation caused by the dominant samples and improve the recognition performance of the classifier on the class-imbalanced dataset. The experimental results on four types of imbalanced datasets show that in the presence of 40% unlabeled samples, the average recognition accuracy for five emitters is improved by 5.34% and 2.69%, respectively, compared with the cross-entropy loss and focus loss. This provides a new idea for solving the problem of SEI under the conditions of insufficient data labels and an unbalanced distribution of data. This paper proposes an SEI method based on cost-sensitive learning and semisupervised generative adversarial networks to address the problem of incomplete sample labels and imbalanced data category distribution in Specific Emitter Identification (SEI), which leads to a decline in inaccuracy. Through semisupervised training, the method optimizes the network parameters of the generator and discriminator, adds a multiscale topological block to ResNet to fuse the multi-dimensional resolution features of the time-domain signal, and attributes additional labels to the generated samples to directly use the discriminator to complete the classification. Simultaneously, a cost-sensitive loss is designed to alleviate the imbalance of gradient propagation caused by the dominant samples and improve the recognition performance of the classifier on the class-imbalanced dataset. The experimental results on four types of imbalanced datasets show that in the presence of 40% unlabeled samples, the average recognition accuracy for five emitters is improved by 5.34% and 2.69%, respectively, compared with the cross-entropy loss and focus loss. This provides a new idea for solving the problem of SEI under the conditions of insufficient data labels and an unbalanced distribution of data.
Combining Terahertz (THz) and Orbital Angular Momentum (OAM) technologies has great potential in high-speed wireless communication. Theoretically, OAM with different modes has strict orthogonality. The communication capacity of the system will improve significantly if OAM technology is applied to the THz communication system. Thus, the manner to generate a high-quality and dynamically controllable THz-OAM beam has been of significant interest to researchers in related fields. In this study, a double-layer transmissive metasurface that uses 3D printing as the processing method with a low cost and processing difficulty is designed. Note that the height of the unit cell for constructing the metasurface is configurable. As the height changes continuously, the phase of the transmitted wave covers 0~2\begin{document}${\pi }$\end{document} within 90~110 GHz, while the transmittance of the units is always higher than 88%. At 100 GHz, which is fed by a WR-10 standard waveguide horn antenna, OAM beams with different modes are generated by changing the relative rotation angle between the double-layer metasurface. The simulation results show that the metasurface antenna designed in this study can achieve OAM beams of \begin{document}$ l=1, \mathrm{2,3} $\end{document}, and the two-dimensional amplitude and phase results correspond with the characteristics of the corresponding modes. When \begin{document}$ l=1,\mathrm{ }2,\mathrm{ }3 $\end{document}, the OAM beam’s modal purity is 85.4%, 84.9%, and 83.4%, respectively. The measurement results include the results at frequency points of 90, 100, and 110 GHz. The results show that the OAM beam has a high-quality bandwidth of 20 GHz, which indicates that the metasurface antenna designed in this study has a wide working bandwidth at a high frequency and can be applied to high-frequency OAM communication. Combining Terahertz (THz) and Orbital Angular Momentum (OAM) technologies has great potential in high-speed wireless communication. Theoretically, OAM with different modes has strict orthogonality. The communication capacity of the system will improve significantly if OAM technology is applied to the THz communication system. Thus, the manner to generate a high-quality and dynamically controllable THz-OAM beam has been of significant interest to researchers in related fields. In this study, a double-layer transmissive metasurface that uses 3D printing as the processing method with a low cost and processing difficulty is designed. Note that the height of the unit cell for constructing the metasurface is configurable. As the height changes continuously, the phase of the transmitted wave covers 0~2\begin{document}${\pi }$\end{document} within 90~110 GHz, while the transmittance of the units is always higher than 88%. At 100 GHz, which is fed by a WR-10 standard waveguide horn antenna, OAM beams with different modes are generated by changing the relative rotation angle between the double-layer metasurface. The simulation results show that the metasurface antenna designed in this study can achieve OAM beams of \begin{document}$ l=1, \mathrm{2,3} $\end{document}, and the two-dimensional amplitude and phase results correspond with the characteristics of the corresponding modes. When \begin{document}$ l=1,\mathrm{ }2,\mathrm{ }3 $\end{document}, the OAM beam’s modal purity is 85.4%, 84.9%, and 83.4%, respectively. The measurement results include the results at frequency points of 90, 100, and 110 GHz. The results show that the OAM beam has a high-quality bandwidth of 20 GHz, which indicates that the metasurface antenna designed in this study has a wide working bandwidth at a high frequency and can be applied to high-frequency OAM communication.
Video Synthetic Aperture Radar (SAR) presents great potential in ground moving target detection and tracking through high frame rate and high-resolution imaging. Target Doppler energy is essential for traditional SAR Ground Moving Target Indication (SAR-GMTI), as the target shadow can also be used for detection in video SAR. However, neither of these detection methods can stand alone to achieve robust detection in video SAR, owing to the distortion or smearing of target energy and its shadow. This paper presents the processing results of airborne video SAR real data using the Faster Region-based Convolutional Neural Network (Faster R-CNN) and the traditional track association based on dual-domain joint detection as proposed in the literature. These two approaches successfully utilize the feature and space time information of target Doppler energy and shadow in the detection of a maneuvering target. Video Synthetic Aperture Radar (SAR) presents great potential in ground moving target detection and tracking through high frame rate and high-resolution imaging. Target Doppler energy is essential for traditional SAR Ground Moving Target Indication (SAR-GMTI), as the target shadow can also be used for detection in video SAR. However, neither of these detection methods can stand alone to achieve robust detection in video SAR, owing to the distortion or smearing of target energy and its shadow. This paper presents the processing results of airborne video SAR real data using the Faster Region-based Convolutional Neural Network (Faster R-CNN) and the traditional track association based on dual-domain joint detection as proposed in the literature. These two approaches successfully utilize the feature and space time information of target Doppler energy and shadow in the detection of a maneuvering target.
Low-altitude small targets, represented by rotor unmanned aerial vehicles, always adopt slow move-and-stop strategy or employ an obstacle blocking strategy to avoid radar detection and conduct point-and-point strikes or interference on important information equipment and strategic bases. This type of target can appear and disappear from the radar Field of View (FoV) multiple times, thus, it is referred to as move-stop-move targets. Dealing with this type of target using traditional tracking models and algorithms can lead to discontinuities in target identity and track fragmentation. To this end, this study investigates the tracking problem of move-stop-move targets with the Labeled Multi-Bernoulli (LMB) filter based on random finite set statistics. To describe the evolution characteristics of multiple entries to the radar FoV, first, we introduce the third type of birth procedure, that is, the Re-Birth (RB) procedure. Specifically, based on the spatial and kinematic relationships between target states before and after returning to the radar FoV, a Spatial Correlation-based RB (SC-RB) procedure is proposed. Then, in the framework of Bayesian filtering, we derive the SC-RB-LMB filter with the proposed SC-RB model, which is capable of tracking move-stop-move targets continuously with its identity unchanged. In typical low-altitude surveillance scenarios, the effectiveness of the proposed model and algorithm is highlighted. Low-altitude small targets, represented by rotor unmanned aerial vehicles, always adopt slow move-and-stop strategy or employ an obstacle blocking strategy to avoid radar detection and conduct point-and-point strikes or interference on important information equipment and strategic bases. This type of target can appear and disappear from the radar Field of View (FoV) multiple times, thus, it is referred to as move-stop-move targets. Dealing with this type of target using traditional tracking models and algorithms can lead to discontinuities in target identity and track fragmentation. To this end, this study investigates the tracking problem of move-stop-move targets with the Labeled Multi-Bernoulli (LMB) filter based on random finite set statistics. To describe the evolution characteristics of multiple entries to the radar FoV, first, we introduce the third type of birth procedure, that is, the Re-Birth (RB) procedure. Specifically, based on the spatial and kinematic relationships between target states before and after returning to the radar FoV, a Spatial Correlation-based RB (SC-RB) procedure is proposed. Then, in the framework of Bayesian filtering, we derive the SC-RB-LMB filter with the proposed SC-RB model, which is capable of tracking move-stop-move targets continuously with its identity unchanged. In typical low-altitude surveillance scenarios, the effectiveness of the proposed model and algorithm is highlighted.
The sample scarcity issue is still challenged for SAR images interpretation. The number of geospatial targets related images is constrained of the SAR images interpretation ability of data acquisition, sample labeling, and the lack of target coverage. Our SAR-ATR method is demonstrated based on scattering information and meta-learning. First, the discrete distribution of the spatial structure of different types of aircraft is quite different in SAR images. An associated scattering classifier is designed to guide the network to learn more discriminative intra-class and inter-class feature descriptions. Our proposed classifier facilitates the modeling of discrete degree of the aircraft target quantitatively and balance the weights of sample pairs dynamically through the differentiated analysis of different target discrete distributions. In addition, an adaptive feature refinement module is designed to optimize the network cohesion for the key parts of the aircraft and reduce the interference of background noise. The proposed method integrates the target scattering distribution properties to the network learning process. On 5-way 1-shot emerging categorized recognition task involved only few samples, our experimental results demonstrate that the recognition accuracy of this method is 59.90%, which is 3.85% higher than the benchmark. After reducing the amount of training data by half, the proposed method is still competitive on the new category of few-shot recognition tasks. The sample scarcity issue is still challenged for SAR images interpretation. The number of geospatial targets related images is constrained of the SAR images interpretation ability of data acquisition, sample labeling, and the lack of target coverage. Our SAR-ATR method is demonstrated based on scattering information and meta-learning. First, the discrete distribution of the spatial structure of different types of aircraft is quite different in SAR images. An associated scattering classifier is designed to guide the network to learn more discriminative intra-class and inter-class feature descriptions. Our proposed classifier facilitates the modeling of discrete degree of the aircraft target quantitatively and balance the weights of sample pairs dynamically through the differentiated analysis of different target discrete distributions. In addition, an adaptive feature refinement module is designed to optimize the network cohesion for the key parts of the aircraft and reduce the interference of background noise. The proposed method integrates the target scattering distribution properties to the network learning process. On 5-way 1-shot emerging categorized recognition task involved only few samples, our experimental results demonstrate that the recognition accuracy of this method is 59.90%, which is 3.85% higher than the benchmark. After reducing the amount of training data by half, the proposed method is still competitive on the new category of few-shot recognition tasks.
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The discontinuous spectrum radar signal is a featured cognitive radar signal. Its spectrum is discontinuous and comprises multiple discrete frequency bands, and the distribution structure of the discrete frequency bands can be adapted to the change of external interference adequately. Therefore, this segmented signal is suitable for dense interference and congested spectrum based spectrum scenarios. The design of discontinuous spectrum signal is focused on two issues: (1) the optimal selection of the discontinuous spectrum structure in accordance with the interference environment to meet the requirements of radar anti-jamming and resolution performance, and (2) the solution of the time-domain emission based on the optimal discontinuous spectrum signal. A typical application of discontinuous spectrum radar signals is the anti-co-frequency interference derived of high-frequency radar. With the upgrade of electronic countermeasures and the spectrum congestion problem caused by the coexistence of multiple electronic devices, discontinuous spectrum signals are used in radar anti-jamming and electromagnetic spectrum compatibility. This paper discusses and summarizes the research on discontinuous signal design criteria and constraints, working frequency band selection and shaping, and time-domain signal waveform synthesis to promote the research and application of discontinuous spectrum signals. The discontinuous spectrum radar signal is a featured cognitive radar signal. Its spectrum is discontinuous and comprises multiple discrete frequency bands, and the distribution structure of the discrete frequency bands can be adapted to the change of external interference adequately. Therefore, this segmented signal is suitable for dense interference and congested spectrum based spectrum scenarios. The design of discontinuous spectrum signal is focused on two issues: (1) the optimal selection of the discontinuous spectrum structure in accordance with the interference environment to meet the requirements of radar anti-jamming and resolution performance, and (2) the solution of the time-domain emission based on the optimal discontinuous spectrum signal. A typical application of discontinuous spectrum radar signals is the anti-co-frequency interference derived of high-frequency radar. With the upgrade of electronic countermeasures and the spectrum congestion problem caused by the coexistence of multiple electronic devices, discontinuous spectrum signals are used in radar anti-jamming and electromagnetic spectrum compatibility. This paper discusses and summarizes the research on discontinuous signal design criteria and constraints, working frequency band selection and shaping, and time-domain signal waveform synthesis to promote the research and application of discontinuous spectrum signals.
Radar and communication systems are hosted on the same platform in many civilian and military applications. Traditionally, radar and communication systems are separately designed, which increases the system size, cost, and power consumption, and decreases the electromagnetic compatibility. Joint radar and communication designs, which have drawn much attention from both the academic and industrial circles, overcome these problems by implementing radar and communication systems using the same hardware. Joint radar and communications systems can be realized by resource allocation and waveform sharing. Waveform sharing schemes have become popular in recent years because they have higher spectral and power efficiency and can fundamentally avoid interference between the different systems. This paper studies the existing strategies of shared waveforms for joint radar and communications systems. The existing strategies are divided into three categories, namely: the communication waveform-based approaches, the radar waveform-based methods, and the joint design schemes. The performance bounds of the joint radar and communication systems are also reviewed to reveal the trade-off between the performance metrics of radar and communications in these systems. The potential for future research into joint radar and communication designs is also discussed. Radar and communication systems are hosted on the same platform in many civilian and military applications. Traditionally, radar and communication systems are separately designed, which increases the system size, cost, and power consumption, and decreases the electromagnetic compatibility. Joint radar and communication designs, which have drawn much attention from both the academic and industrial circles, overcome these problems by implementing radar and communication systems using the same hardware. Joint radar and communications systems can be realized by resource allocation and waveform sharing. Waveform sharing schemes have become popular in recent years because they have higher spectral and power efficiency and can fundamentally avoid interference between the different systems. This paper studies the existing strategies of shared waveforms for joint radar and communications systems. The existing strategies are divided into three categories, namely: the communication waveform-based approaches, the radar waveform-based methods, and the joint design schemes. The performance bounds of the joint radar and communication systems are also reviewed to reveal the trade-off between the performance metrics of radar and communications in these systems. The potential for future research into joint radar and communication designs is also discussed.
Waveform Optimizaion Design
The pseudo-random agility technology for interpulse waveform parameters in airborne radar increases the complexity and uncertainty of radar waveform and improves its anti-clutter and anti-interference ability by optimizing the pulse repetition interval, initial phase, frequency, and amplitude, which is one of the main developmental directions of airborne radar technology. The pseudo-random agility of interpulse parameters makes multi-pulse coherent accumulation and modeling of clutter spectrum characteristics difficult. In this paper, a pseudo-random agility signal model of interpulse parameters is established. Furthermore, a non-uniform parameter coherent processing method is proposed, and the anti-interference performance is analyzed. Based on the analysis, the clutter echo model of airborne radar with random pulse repetition interval is studied, and a joint transmitter-receiver filter design is proposed for strong clutter processing. Finally, numerical simulation is conducted to verify the results. The pseudo-random agility technology for interpulse waveform parameters in airborne radar increases the complexity and uncertainty of radar waveform and improves its anti-clutter and anti-interference ability by optimizing the pulse repetition interval, initial phase, frequency, and amplitude, which is one of the main developmental directions of airborne radar technology. The pseudo-random agility of interpulse parameters makes multi-pulse coherent accumulation and modeling of clutter spectrum characteristics difficult. In this paper, a pseudo-random agility signal model of interpulse parameters is established. Furthermore, a non-uniform parameter coherent processing method is proposed, and the anti-interference performance is analyzed. Based on the analysis, the clutter echo model of airborne radar with random pulse repetition interval is studied, and a joint transmitter-receiver filter design is proposed for strong clutter processing. Finally, numerical simulation is conducted to verify the results.
Multinode transceiver division systems can cooperate across multiple domains, including space, time, frequency, and energy, through waveforms. Moreover, it can provide greater anti-interference degrees of freedom than that from a single radar. Through this paper, we propose a frequency domain cooperative waveform design method based on two short baseline transceiver separation systems to resist multi-mainlobe interference. First, a narrowband detection signal with a locally good autocorrelation level was optimized using the Majorization-Minimization-based Proximal Method of Multipliers (MM-PMM) algorithm. Then, according to the characteristics of the frequency hopping of the narrowband detection signal, the corresponding wideband signal with a null spectrum was optimized as the cover signal of a narrowband signal. Further, two transmitting nodes were used to transmit the narrowband and wideband signals. Finally, a signal processing method based on phase-coherent and nonphase-coherent joint accumulation, with known frequency agility was used to process the cooperative waveform in the frequency domain. Numerical simulation results demonstrated the convergence of the MM-PMM algorithm, principle of frequency domain cover, and effectiveness of the frequency domain cooperative waveform design method against multi-mainlobe interference. Multinode transceiver division systems can cooperate across multiple domains, including space, time, frequency, and energy, through waveforms. Moreover, it can provide greater anti-interference degrees of freedom than that from a single radar. Through this paper, we propose a frequency domain cooperative waveform design method based on two short baseline transceiver separation systems to resist multi-mainlobe interference. First, a narrowband detection signal with a locally good autocorrelation level was optimized using the Majorization-Minimization-based Proximal Method of Multipliers (MM-PMM) algorithm. Then, according to the characteristics of the frequency hopping of the narrowband detection signal, the corresponding wideband signal with a null spectrum was optimized as the cover signal of a narrowband signal. Further, two transmitting nodes were used to transmit the narrowband and wideband signals. Finally, a signal processing method based on phase-coherent and nonphase-coherent joint accumulation, with known frequency agility was used to process the cooperative waveform in the frequency domain. Numerical simulation results demonstrated the convergence of the MM-PMM algorithm, principle of frequency domain cover, and effectiveness of the frequency domain cooperative waveform design method against multi-mainlobe interference.
Radar-communication integration is an effective way to solve the congestion problem of spectrum resource. Sharing waveform design is the key technology that realizes the radar and communication functions simultaneously. This study solves the multicarrier waveform optimization problem for an Intelligent Reflecting Surface (IRS)-assisted Dual-function Radar-Communication (DRC) system. First, by maximizing Radar Mutual Information (RMI) along with the constraints of transmission power, Word Error Probability (WEP), sidelobe amplitude and IRS reflection coefficient, a joint optimization model with dual-functional transmit waveform, IRS reflecting units, radar and communication receiving beampattern is constructed. Second, a multicarrier waveform optimization algorithm based on Alternating Direction Maximization (ADM) is proposed. The original non-convex optimization problem is decomposed into several low-complexity subproblems and then iteratively optimized to obtain the local power allocation strategy of the multicarrier waveform. Finally, the simulation results show that the radar and communication functions can be simultaneously realized using the ADM algorithm. For the IRS-assisted DRC system, both the radar and communication performances can be effectively improved compared with those of the existing methods. Radar-communication integration is an effective way to solve the congestion problem of spectrum resource. Sharing waveform design is the key technology that realizes the radar and communication functions simultaneously. This study solves the multicarrier waveform optimization problem for an Intelligent Reflecting Surface (IRS)-assisted Dual-function Radar-Communication (DRC) system. First, by maximizing Radar Mutual Information (RMI) along with the constraints of transmission power, Word Error Probability (WEP), sidelobe amplitude and IRS reflection coefficient, a joint optimization model with dual-functional transmit waveform, IRS reflecting units, radar and communication receiving beampattern is constructed. Second, a multicarrier waveform optimization algorithm based on Alternating Direction Maximization (ADM) is proposed. The original non-convex optimization problem is decomposed into several low-complexity subproblems and then iteratively optimized to obtain the local power allocation strategy of the multicarrier waveform. Finally, the simulation results show that the radar and communication functions can be simultaneously realized using the ADM algorithm. For the IRS-assisted DRC system, both the radar and communication performances can be effectively improved compared with those of the existing methods.
Radar waveform optimization has recently drawn much attention. The radar waveform possesses not only constant amplitude but also a low autocorrelation sidelobe level. However, because of the presence of the constant modular constraint, the problem of optimizing the waveform is non-convex, which is difficult to address. The feasible domain used by the current methods usually contains the vector space with two dimensions: amplitude and phase. The optimization procedures accompanied by the constant constraint enlarge the difficulty and amount of calculation. Herein, the problem of designing unimodular sequences with low autocorrelation sidelobes is addressed, and a novel approach based on phase optimization is presented. The feasible domain is compressed into the vector space with only the phase dimension. The proposed method conducts a deep analysis of the relationship between the phases of the elements in the unimodular sequence and successively updates the vector with a closed-form solution in an element-by-element manner at each iteration using the coordinate descent method, which comprises low computational complexity. By compressing the feasible domain and updating the vector variable using the closed solution, the integrated sidelobe level and computation efficiency are improved. Representative numerical simulations are provided to verify the effectiveness of the proposed method. Radar waveform optimization has recently drawn much attention. The radar waveform possesses not only constant amplitude but also a low autocorrelation sidelobe level. However, because of the presence of the constant modular constraint, the problem of optimizing the waveform is non-convex, which is difficult to address. The feasible domain used by the current methods usually contains the vector space with two dimensions: amplitude and phase. The optimization procedures accompanied by the constant constraint enlarge the difficulty and amount of calculation. Herein, the problem of designing unimodular sequences with low autocorrelation sidelobes is addressed, and a novel approach based on phase optimization is presented. The feasible domain is compressed into the vector space with only the phase dimension. The proposed method conducts a deep analysis of the relationship between the phases of the elements in the unimodular sequence and successively updates the vector with a closed-form solution in an element-by-element manner at each iteration using the coordinate descent method, which comprises low computational complexity. By compressing the feasible domain and updating the vector variable using the closed solution, the integrated sidelobe level and computation efficiency are improved. Representative numerical simulations are provided to verify the effectiveness of the proposed method.
Waveform-based Anti-Jamming
To suppress interrupted sampling jamming, a joint waveform and filter design method was proposed in this study. A fast algorithm was also suggested to design sequences with large code lengths. The joint design problem was formulated on the basis of the penalty function and Pareto optimization method. By deriving the theoretical solution of the trace of matrices in each iteration, the computation order of the proposed algorithm was decreased significantly compared to that of the conventional algorithm. Moreover, an accelerated scheme based on the squared iterative method, which further improves the running speed of the algorithm, was proposed. Simulation results demonstrate that the proposed algorithm runs faster than traditional algorithms. Moreover, the designed waveform and filter can be applied to suppress interrupted sampling repeater jamming when the false targets are inseparable from the true targets in time-frequency domains. To suppress interrupted sampling jamming, a joint waveform and filter design method was proposed in this study. A fast algorithm was also suggested to design sequences with large code lengths. The joint design problem was formulated on the basis of the penalty function and Pareto optimization method. By deriving the theoretical solution of the trace of matrices in each iteration, the computation order of the proposed algorithm was decreased significantly compared to that of the conventional algorithm. Moreover, an accelerated scheme based on the squared iterative method, which further improves the running speed of the algorithm, was proposed. Simulation results demonstrate that the proposed algorithm runs faster than traditional algorithms. Moreover, the designed waveform and filter can be applied to suppress interrupted sampling repeater jamming when the false targets are inseparable from the true targets in time-frequency domains.
As a typical coherent jamming method, Interrupted Sampling Repeater Jamming (ISRJ) can generate multiple false targets with high fidelity at the radar receiver using under-sampling, which causes real targets detection to become invalid. To solve this problem, an anti-ISRJ method based on the joint design of Doppler-tolerant complementary sequences and receiving filters is proposed in this paper. First, by considering the Doppler tolerance of sequences, the sum of the energy of the ambiguity function of transmitted sequences and receiving filters and energy of the ambiguity function of ISRJ signals and receiving filters is chosen as the objective function. Meanwhile, the constant modulus constraint on sequences and signal-to-noise ratio constraint are considered. Then, an alternately iterative algorithm based on the Majorization-Minimization (MM) method is proposed to solve the non-convex optimization problem. Finally, numerical results are presented as a way to compare with conventional methods, and the sequences and receiving filters designed by the proposed method show better pulse compression correlation and anti-ISRJ performance. These procedures can substantially improve the radar detection ability to move targets in the jamming scene. As a typical coherent jamming method, Interrupted Sampling Repeater Jamming (ISRJ) can generate multiple false targets with high fidelity at the radar receiver using under-sampling, which causes real targets detection to become invalid. To solve this problem, an anti-ISRJ method based on the joint design of Doppler-tolerant complementary sequences and receiving filters is proposed in this paper. First, by considering the Doppler tolerance of sequences, the sum of the energy of the ambiguity function of transmitted sequences and receiving filters and energy of the ambiguity function of ISRJ signals and receiving filters is chosen as the objective function. Meanwhile, the constant modulus constraint on sequences and signal-to-noise ratio constraint are considered. Then, an alternately iterative algorithm based on the Majorization-Minimization (MM) method is proposed to solve the non-convex optimization problem. Finally, numerical results are presented as a way to compare with conventional methods, and the sequences and receiving filters designed by the proposed method show better pulse compression correlation and anti-ISRJ performance. These procedures can substantially improve the radar detection ability to move targets in the jamming scene.
Intermittent sampling noise modulation and forward jamming is a novel active jamming method with both suppression and deception characteristics and is a challenge often encountered in radar anti-jamming. To improve the capability of frequency-agile radar to resist noise-modulated Intermittent Sampling Repeater Jamming (ISRJ), we propose an anti-ISRJ method based on frequency-agile radar joint Fuzzy C-Means (FCM). First, we designed a radar-transmitted waveform with intra pulse frequency coding and inter pulse frequency agility. Second, after receiving the echo signal, we obtained the sub pulse signals corresponding to different intra-pulse frequency codes via narrow-band filtering in the frequency domain. Third, we adopted the FCM algorithm to determine the presence of ISRJs in the sub pulses after pulse compression. Finally, we realized the phase-coherent accumulation of inter pulse frequency-hopping waveform using the compressed sensing algorithm. Theoretical analysis and simulation experiments showed that the proposed method can effectively resist ISRJ. Intermittent sampling noise modulation and forward jamming is a novel active jamming method with both suppression and deception characteristics and is a challenge often encountered in radar anti-jamming. To improve the capability of frequency-agile radar to resist noise-modulated Intermittent Sampling Repeater Jamming (ISRJ), we propose an anti-ISRJ method based on frequency-agile radar joint Fuzzy C-Means (FCM). First, we designed a radar-transmitted waveform with intra pulse frequency coding and inter pulse frequency agility. Second, after receiving the echo signal, we obtained the sub pulse signals corresponding to different intra-pulse frequency codes via narrow-band filtering in the frequency domain. Third, we adopted the FCM algorithm to determine the presence of ISRJs in the sub pulses after pulse compression. Finally, we realized the phase-coherent accumulation of inter pulse frequency-hopping waveform using the compressed sensing algorithm. Theoretical analysis and simulation experiments showed that the proposed method can effectively resist ISRJ.
To improve radar’s anti-Interrupted Sampling Repeater Jamming (ISRJ) capability, this study proposes a parallel interference suppression method based on the fractional Fourier transform, which uses the “active” anti-jamming capability of the interpulse and intrapulse frequency-agile waveform according to the characteristics of ISRJ transceiver splitting. First, the interfered sub-pulses are extracted in the time domain, and the extracted signals are sliced. Then, the narrowband filter banks are used to suppress the interference in the fractional Fourier domain. Finally, matching filter banks are constructed to achieve subpulse integration by applying segmented pulse compression. The theoretical analysis and simulation results show that the proposed method effectively suppresses multi-mainlobe interferences comprising different types of ISRJ and exhibits good anti-interference performance under a high jamming-to-signal ratio, which considerably improves the anti-jamming capability of the radar. To improve radar’s anti-Interrupted Sampling Repeater Jamming (ISRJ) capability, this study proposes a parallel interference suppression method based on the fractional Fourier transform, which uses the “active” anti-jamming capability of the interpulse and intrapulse frequency-agile waveform according to the characteristics of ISRJ transceiver splitting. First, the interfered sub-pulses are extracted in the time domain, and the extracted signals are sliced. Then, the narrowband filter banks are used to suppress the interference in the fractional Fourier domain. Finally, matching filter banks are constructed to achieve subpulse integration by applying segmented pulse compression. The theoretical analysis and simulation results show that the proposed method effectively suppresses multi-mainlobe interferences comprising different types of ISRJ and exhibits good anti-interference performance under a high jamming-to-signal ratio, which considerably improves the anti-jamming capability of the radar.

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