Current Issue

2021 Vol. 10, No. 6
Special Topic Papers: Waveform Diversity Array Radar Technology
It is difficult for the traditional radar to suppress deceptive mainlobe interference and separate the range ambiguous clutter. The proposal of a waveform diverse array changes the way of obtaining information through utilizing degrees-of-freedom in the transmit dimension. Through flexible system design and signal processing methods, this array enhances the ability of information extraction and improves the anti-jamming and detection performance, compared with the traditional phased array and Multiple-Input Multiple-Output (MIMO) radar. This paper summarizes the research progress of waveform diverse array radars in China and overseas and provides the basic concepts of the array diversity system regarding frequency, time, and phase modulation. Furthermore, the research trend of waveform diverse array radars has been discussed. Based on the existing basic theory and key technology research, the advantages of a waveform diverse array in providing new information about targets and increasing the additional controllable degrees-of-freedom of the system are verified, thereby improving the multidimensional detection capability of the new radar system. It is difficult for the traditional radar to suppress deceptive mainlobe interference and separate the range ambiguous clutter. The proposal of a waveform diverse array changes the way of obtaining information through utilizing degrees-of-freedom in the transmit dimension. Through flexible system design and signal processing methods, this array enhances the ability of information extraction and improves the anti-jamming and detection performance, compared with the traditional phased array and Multiple-Input Multiple-Output (MIMO) radar. This paper summarizes the research progress of waveform diverse array radars in China and overseas and provides the basic concepts of the array diversity system regarding frequency, time, and phase modulation. Furthermore, the research trend of waveform diverse array radars has been discussed. Based on the existing basic theory and key technology research, the advantages of a waveform diverse array in providing new information about targets and increasing the additional controllable degrees-of-freedom of the system are verified, thereby improving the multidimensional detection capability of the new radar system.

Frequency Diverse Array (FDA) Multiple-Input Multiple-Output (MIMO) radar equipped with a FDA can possess beam patterns that are dependent on range, angle, and time, and it can increase the degree of freedom. This paper introduces a conformal array attached to the surface of the carrier, the array can reduce the aerodynamic impact on the carrier and reduce the cross section of the FDA-MIMO radar. First, the conformal FDA-MIMO measurement model is formulated, and a Cramér-Rao Lower Bound (CRLB) is derived to evaluate the parameter estimation performance. To avoid the three-dimensional search of the traditional three-dimensional MUltiple SIgnal Classification (3D-MUSIC) algorithm, a Reduced-Dimension MUltiple SIgnal Classification (RD-MUSIC) algorithm is proposed for parameter estimation. The simulation results demonstrate that the proposed algorithm has a slightly lower estimation accuracy than the 3D-MUSIC algorithm but a much lower computational complexity. In addition, the proposed algorithm has better range estimation performance for multiple targets than the 3D-MUSIC algorithm.

Frequency Diverse Array (FDA) Multiple-Input Multiple-Output (MIMO) radar equipped with a FDA can possess beam patterns that are dependent on range, angle, and time, and it can increase the degree of freedom. This paper introduces a conformal array attached to the surface of the carrier, the array can reduce the aerodynamic impact on the carrier and reduce the cross section of the FDA-MIMO radar. First, the conformal FDA-MIMO measurement model is formulated, and a Cramér-Rao Lower Bound (CRLB) is derived to evaluate the parameter estimation performance. To avoid the three-dimensional search of the traditional three-dimensional MUltiple SIgnal Classification (3D-MUSIC) algorithm, a Reduced-Dimension MUltiple SIgnal Classification (RD-MUSIC) algorithm is proposed for parameter estimation. The simulation results demonstrate that the proposed algorithm has a slightly lower estimation accuracy than the 3D-MUSIC algorithm but a much lower computational complexity. In addition, the proposed algorithm has better range estimation performance for multiple targets than the 3D-MUSIC algorithm.

A Frequency Diverse Array (FDA) radar achieves the uniform coverage of a large-scale airspace within a pulse duration by introducing a designed carrier frequency increment between adjacent transmitting elements. Unfortunately, the beam scanning property of an FDA radar leads to a reduction in dwell time at certain azimuth direction, which results in the deterioration of the range resolution. To solve this problem, we propose a novel coherent FDA radar waveform in the space-time domain, in which spatial phase encoding is introduced between transmitting elements to significantly improve the range resolution of a coherent FDA radar. A nonlinear frequency modulation signal is also used in the time domain to obtain a low-range Peak to SideLobe Ratio (PSLR). Simulation results verify that the proposed FDA radar waveform design realizes both a low PSLR and high range resolution. Finally, we analyze the Doppler sensitivity of our proposed method for the observation of high-speed moving targets. A Frequency Diverse Array (FDA) radar achieves the uniform coverage of a large-scale airspace within a pulse duration by introducing a designed carrier frequency increment between adjacent transmitting elements. Unfortunately, the beam scanning property of an FDA radar leads to a reduction in dwell time at certain azimuth direction, which results in the deterioration of the range resolution. To solve this problem, we propose a novel coherent FDA radar waveform in the space-time domain, in which spatial phase encoding is introduced between transmitting elements to significantly improve the range resolution of a coherent FDA radar. A nonlinear frequency modulation signal is also used in the time domain to obtain a low-range Peak to SideLobe Ratio (PSLR). Simulation results verify that the proposed FDA radar waveform design realizes both a low PSLR and high range resolution. Finally, we analyze the Doppler sensitivity of our proposed method for the observation of high-speed moving targets.
Passive localization technology is an integral part of electronic warfare. However, most methods for countering passive localization systems, such as radio frequency stealth and electronic interference, have limitations. This paper proposes a new passive localization countermeasure method based on Frequency Diverse Array (FDA). The unique beam scanning property reduces dwell time at a certain azimuth direction, making it difficult for a passive localization system to intercept FDA signals for a long time. In contrast, the time-varying characteristics of the FDA considerably reduce the signal-to-noise ratio of the received signal, increasing the difficulty in accurately detecting the localization information of the radiation source. Thus, using this new technology, the electronic system platform can perceive the external environment through the signals radiated by the FDA antenna while deceiving the enemy’s passive localization system. Both theoretical analysis and numerical results showed that FDA transmitted signal achieved significantly better localization countermeasure performance in direction finding by an interferometer, frequency difference of arrival, and time difference of arrival, which are particularly useful for a new generation of electronic systems with reconnaissance detection and passive localization countermeasures capability. Passive localization technology is an integral part of electronic warfare. However, most methods for countering passive localization systems, such as radio frequency stealth and electronic interference, have limitations. This paper proposes a new passive localization countermeasure method based on Frequency Diverse Array (FDA). The unique beam scanning property reduces dwell time at a certain azimuth direction, making it difficult for a passive localization system to intercept FDA signals for a long time. In contrast, the time-varying characteristics of the FDA considerably reduce the signal-to-noise ratio of the received signal, increasing the difficulty in accurately detecting the localization information of the radiation source. Thus, using this new technology, the electronic system platform can perceive the external environment through the signals radiated by the FDA antenna while deceiving the enemy’s passive localization system. Both theoretical analysis and numerical results showed that FDA transmitted signal achieved significantly better localization countermeasure performance in direction finding by an interferometer, frequency difference of arrival, and time difference of arrival, which are particularly useful for a new generation of electronic systems with reconnaissance detection and passive localization countermeasures capability.
Synthetic Aperture Radar
The limitations of satellite versatility, application dimensions, and wide-area observation efficiency are major issues impeding the development of Chinese spaceborne Synthetic Aperture Radar (SAR). China lacks a satellite system that can realize long-term, stable, and high-performance environmental dynamic monitoring oriented to the world. As the international environment becomes increasingly complex, China urgently needs to develop a SAR satellite system for global dynamic environmental monitoring to achieve large-scale, high-revisit, long-term, stable, and high-precision observations. This paper proposes a SAR Constellation with Dense Time-S E ries for MultiDimensional E nvironmental Monitoring of the Earth (See-Earth) plan. In addition, the system conception, technical architectures, performance analysis, application potential, and new system expansion are discussed. The limitations of satellite versatility, application dimensions, and wide-area observation efficiency are major issues impeding the development of Chinese spaceborne Synthetic Aperture Radar (SAR). China lacks a satellite system that can realize long-term, stable, and high-performance environmental dynamic monitoring oriented to the world. As the international environment becomes increasingly complex, China urgently needs to develop a SAR satellite system for global dynamic environmental monitoring to achieve large-scale, high-revisit, long-term, stable, and high-precision observations. This paper proposes a SAR Constellation with Dense Time-S E ries for MultiDimensional E nvironmental Monitoring of the Earth (See-Earth) plan. In addition, the system conception, technical architectures, performance analysis, application potential, and new system expansion are discussed.
Micromotion targets can enable microDoppler modulation on electromagnetic waves owing to their motion components in different directions, resulting in the defocusing effects of the target imaging features along the azimuth direction. This phenomenon has been widely considered and investigated in target recognition and antirecognition. As a research hotspot, electronically controlled time-varying electromagnetic materials have fast modulation characteristics; however, their imaging characteristics have not received much attention. This study investigates the Synthetic Aperture Radar (SAR) modulation characteristics along the range direction of electronically controlled time-varying electromagnetic materials. Then, this study establishes the time-varying electromagnetic material spectrum transformation model and analyzes the SAR target characteristic control principle. Taking the Phase-Switched Screen (PSS) as an example, a nonperiodic PSS phase modulation model is established, and its spectrum has continuous frequency shift characteristics. On this basis, the influence of continuous frequency shift modulation induced by nonperiodic PSS on SAR was further discussed and the defocusing phenomenon of the target along the range direction was detected. Through the simulation of measured data, the validity of the proposed theoretical method is verified. Micromotion targets can enable microDoppler modulation on electromagnetic waves owing to their motion components in different directions, resulting in the defocusing effects of the target imaging features along the azimuth direction. This phenomenon has been widely considered and investigated in target recognition and antirecognition. As a research hotspot, electronically controlled time-varying electromagnetic materials have fast modulation characteristics; however, their imaging characteristics have not received much attention. This study investigates the Synthetic Aperture Radar (SAR) modulation characteristics along the range direction of electronically controlled time-varying electromagnetic materials. Then, this study establishes the time-varying electromagnetic material spectrum transformation model and analyzes the SAR target characteristic control principle. Taking the Phase-Switched Screen (PSS) as an example, a nonperiodic PSS phase modulation model is established, and its spectrum has continuous frequency shift characteristics. On this basis, the influence of continuous frequency shift modulation induced by nonperiodic PSS on SAR was further discussed and the defocusing phenomenon of the target along the range direction was detected. Through the simulation of measured data, the validity of the proposed theoretical method is verified.
Low-oversampled staggered synthetic aperture radar can achieve continuously observed high-resolution and wide-swath imaging by utilizing the variable pulse repetition interval to distribute blind ranges. Moreover, adopting a low oversampling ratio can reduce the data storage requirements, contributing to its research significance. However, non-uniform sampling, echo data loss, and non-ideal Azimuth Antenna Pattern (AAP) cause severe azimuth ambiguities in a directly focused image. This study proposes a compressive sensing-based method with better ambiguity removal performance and higher efficiency compared to existing methods. First, an Innovative Frequency-Domain Model (IFDM) is constructed, which accurately describes the non-uniform sampling, echo data loss, and coupled range cell migration. Based on the IFDM, an optimization problem is constructed and solved by the two-dimensional fast iterative shrinkage thresholding algorithm to remove the ambiguity caused by non-uniform sampling and echo data loss. Subsequently, selective filtering is used to suppress the ambiguity caused by the AAP. The experiments demonstrate that the proposed method can more effectively and efficiently suppress the azimuth ambiguities compared to existing methods. Low-oversampled staggered synthetic aperture radar can achieve continuously observed high-resolution and wide-swath imaging by utilizing the variable pulse repetition interval to distribute blind ranges. Moreover, adopting a low oversampling ratio can reduce the data storage requirements, contributing to its research significance. However, non-uniform sampling, echo data loss, and non-ideal Azimuth Antenna Pattern (AAP) cause severe azimuth ambiguities in a directly focused image. This study proposes a compressive sensing-based method with better ambiguity removal performance and higher efficiency compared to existing methods. First, an Innovative Frequency-Domain Model (IFDM) is constructed, which accurately describes the non-uniform sampling, echo data loss, and coupled range cell migration. Based on the IFDM, an optimization problem is constructed and solved by the two-dimensional fast iterative shrinkage thresholding algorithm to remove the ambiguity caused by non-uniform sampling and echo data loss. Subsequently, selective filtering is used to suppress the ambiguity caused by the AAP. The experiments demonstrate that the proposed method can more effectively and efficiently suppress the azimuth ambiguities compared to existing methods.
In the Synthetic Aperture Radar (SAR) remote sensing image, ships are visually significant targets on the sea surface. Because they are made of metal, thus the backscatter is strong, while the sea surface is smooth and the backscatter is weak. However, the large-width SAR remote sensing image has a complicated sea background, and the features of various ship targets are quite different. To solve this problem, a SAR remote sensing image ship detection model called NanoDet is proposed. NanoDet is based on visual saliency. First, the image samples are divided into various scene categories using an automatic clustering algorithm. Second, differentiated saliency detection is performed for images in various scenes. Finally, the optimized lightweight network model, NanoDet, is used to perform feature learning on the training samples added with the saliency maps, so that the system model can achieve fast and high-precision ship detection effects. This method is helpful for the real-time application of SAR images. The lightweight model is conducive to hardware transplantation in the future.This study conducts experiments based on the public data set SSDD and AIR-SARship-2.0, and the experiments results verify the effectiveness of our approach. In the Synthetic Aperture Radar (SAR) remote sensing image, ships are visually significant targets on the sea surface. Because they are made of metal, thus the backscatter is strong, while the sea surface is smooth and the backscatter is weak. However, the large-width SAR remote sensing image has a complicated sea background, and the features of various ship targets are quite different. To solve this problem, a SAR remote sensing image ship detection model called NanoDet is proposed. NanoDet is based on visual saliency. First, the image samples are divided into various scene categories using an automatic clustering algorithm. Second, differentiated saliency detection is performed for images in various scenes. Finally, the optimized lightweight network model, NanoDet, is used to perform feature learning on the training samples added with the saliency maps, so that the system model can achieve fast and high-precision ship detection effects. This method is helpful for the real-time application of SAR images. The lightweight model is conducive to hardware transplantation in the future.This study conducts experiments based on the public data set SSDD and AIR-SARship-2.0, and the experiments results verify the effectiveness of our approach.
Automatic Target Recognition (ATR) in Synthetic Aperture Radar (SAR) has been extensively applied in military and civilian fields. However, SAR images are very sensitive to the azimuth of the images, as the same target can differ greatly from different aspects. This means that more reliable and robust multiaspect ATR recognition is required. In this paper, we propose a multiaspect ATR model based on EfficientNet and BiGRU. To train this model, we use island loss, which is more suitable for SAR ATR. Experimental results have revealed that our proposed method can achieve 100% accuracy for 10-class recognition on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database. The SAR targets in three special imaging cases with large depression angles, version variants, and configuration variants reached recognition accuracies of 99.68%, 99.95%, and 99.91%, respectively. In addition, the proposed method achieves satisfactory accuracy even with smaller datasets. Our experimental results show that our proposed method outperforms other state-of-the-art ATR methods on most MSTAR datasets and exhibits a certain degree of robustness. Automatic Target Recognition (ATR) in Synthetic Aperture Radar (SAR) has been extensively applied in military and civilian fields. However, SAR images are very sensitive to the azimuth of the images, as the same target can differ greatly from different aspects. This means that more reliable and robust multiaspect ATR recognition is required. In this paper, we propose a multiaspect ATR model based on EfficientNet and BiGRU. To train this model, we use island loss, which is more suitable for SAR ATR. Experimental results have revealed that our proposed method can achieve 100% accuracy for 10-class recognition on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database. The SAR targets in three special imaging cases with large depression angles, version variants, and configuration variants reached recognition accuracies of 99.68%, 99.95%, and 99.91%, respectively. In addition, the proposed method achieves satisfactory accuracy even with smaller datasets. Our experimental results show that our proposed method outperforms other state-of-the-art ATR methods on most MSTAR datasets and exhibits a certain degree of robustness.
Radar Signal and Data Processing
To mitigate Radio Frequency Interference (RFI) for polarimetric Doppler weather radars, this paper proposes to use spectral polarimetric filters. Polarimetric weather radars can be divided into two basic categories: Simultaneously Transmitting and Simultaneously Receiving (STSR) and Alternately Transmitting and Simultaneously Receiving (ATSR). First, the real RFI measurements from an operational C-band STSR weather radar help characterize the temporal, spectral and polarimetric features of RFI. Then, RFI is simulated in an X-band ATSR radar to quantify the performances of spectral polarimetric filters. Overall, spectral polarimetric filters can keep the precipitation and remove RFI in an ATSR radar. Finally, the data division method is put forward for STSR radars by mimicking the ATSR measurements. Good performance in RFI mitigation is also verified by using the same spectral polarimetric filters. To mitigate Radio Frequency Interference (RFI) for polarimetric Doppler weather radars, this paper proposes to use spectral polarimetric filters. Polarimetric weather radars can be divided into two basic categories: Simultaneously Transmitting and Simultaneously Receiving (STSR) and Alternately Transmitting and Simultaneously Receiving (ATSR). First, the real RFI measurements from an operational C-band STSR weather radar help characterize the temporal, spectral and polarimetric features of RFI. Then, RFI is simulated in an X-band ATSR radar to quantify the performances of spectral polarimetric filters. Overall, spectral polarimetric filters can keep the precipitation and remove RFI in an ATSR radar. Finally, the data division method is put forward for STSR radars by mimicking the ATSR measurements. Good performance in RFI mitigation is also verified by using the same spectral polarimetric filters.
When airborne radar is applied to the non-side-looking mode, moving target detection performance considerably degrades because of the nonstationary clutter. Conventional three-dimensional (3D) Space-Time Adaptive Processing (STAP) can effectively eliminate the nonstationary clutter via adaptively constructing an elevation-azimuth-Doppler 3D filter. However, large system degrees of freedom lead to a shortage of training samples in a heterogeneous environment. Although introducing the Sparse Recovery (SR) technology substantially reduces the sample requirement, the practical application of this technology is limited by computational complexities. To solve the above problems, this paper proposes a fast 3D sparse Bayesian learning STAP, based on the third-order tensor structure of echo data. In the proposed method, large-scale matrix calculation is decomposed into small-scale matrix calculation using a low-complexity tensor-based operation, thus considerably reducing the computational load. Exhaustive numerical experiments verify that the proposed method directly reduces the computational load by several orders of magnitude compared with that of the existing SR-STAP algorithms, while maintaining the SR-STAP performance. Therefore, the tensor-based method is a superior processing method than the vector-based method in engineering. When airborne radar is applied to the non-side-looking mode, moving target detection performance considerably degrades because of the nonstationary clutter. Conventional three-dimensional (3D) Space-Time Adaptive Processing (STAP) can effectively eliminate the nonstationary clutter via adaptively constructing an elevation-azimuth-Doppler 3D filter. However, large system degrees of freedom lead to a shortage of training samples in a heterogeneous environment. Although introducing the Sparse Recovery (SR) technology substantially reduces the sample requirement, the practical application of this technology is limited by computational complexities. To solve the above problems, this paper proposes a fast 3D sparse Bayesian learning STAP, based on the third-order tensor structure of echo data. In the proposed method, large-scale matrix calculation is decomposed into small-scale matrix calculation using a low-complexity tensor-based operation, thus considerably reducing the computational load. Exhaustive numerical experiments verify that the proposed method directly reduces the computational load by several orders of magnitude compared with that of the existing SR-STAP algorithms, while maintaining the SR-STAP performance. Therefore, the tensor-based method is a superior processing method than the vector-based method in engineering.
Target Feature Extraction
As a stable and widely covered signal resource, a Global Navigation Satellite System (GNSS) plays an important part in micro-Doppler extraction in a near field. This paper aims at the problems associated with rotor blade target recognition and proposes a novel solution based on phase compensation. First, the mathematical mechanism of flicker distribution in a time-frequency field is analyzed, and phase compensation is used to achieve the Doppler focusing, and then the blade number can be estimated. Second, according to the that the minimum delta frequency principle between the center frequency and standard frequency, the rotation velocity of the rotor blade is obtained. Next, blade lengths can be calculated through the flicker bandwidth in the time-frequency domain. Finally, the simulation experiments validate the effectiveness of the proposed method. As a stable and widely covered signal resource, a Global Navigation Satellite System (GNSS) plays an important part in micro-Doppler extraction in a near field. This paper aims at the problems associated with rotor blade target recognition and proposes a novel solution based on phase compensation. First, the mathematical mechanism of flicker distribution in a time-frequency field is analyzed, and phase compensation is used to achieve the Doppler focusing, and then the blade number can be estimated. Second, according to the that the minimum delta frequency principle between the center frequency and standard frequency, the rotation velocity of the rotor blade is obtained. Next, blade lengths can be calculated through the flicker bandwidth in the time-frequency domain. Finally, the simulation experiments validate the effectiveness of the proposed method.
A recognition method combining Cameron decomposition and fusing Reduced Kernel Extreme Learning Machine (RKELM) is proposed for the Full Polarimetric (FP) High Resolution Range Profile (HRRP)-based radar target recognition task. In the feature extraction phase, Cameron decomposition is exploited to define the projection component of the target on the standard scatterers. Through analysis, the projection components on three scattering bases, i.e., trihedral, dihedral, and 1/4 wave device, are selected as target features, which achieve more detailed descriptions of the target characteristics. In the classification phase, considering the instability of the recognition performance of the RKELM algorithm, the RKELM based on prototype clustering preprocessing is first proposed. Then, to improve the recognition performance, we proposed the feature level fusing RKELM and the decision level fusing RKELM to fuse the three projection components of the targets. The experiments compared the performance of the proposed recognition method and the state-of-the-art methods using the FP HRRP data from 10 civilian vehicles. The results demonstrate that the projection features by Cameron decomposition exhibit higher separability and better noise robustness, and that the feature level fusing RKELM has better generalization performance with a large number of training samples, but the decision level fusing RKELM was better with a small number of training samples. A recognition method combining Cameron decomposition and fusing Reduced Kernel Extreme Learning Machine (RKELM) is proposed for the Full Polarimetric (FP) High Resolution Range Profile (HRRP)-based radar target recognition task. In the feature extraction phase, Cameron decomposition is exploited to define the projection component of the target on the standard scatterers. Through analysis, the projection components on three scattering bases, i.e., trihedral, dihedral, and 1/4 wave device, are selected as target features, which achieve more detailed descriptions of the target characteristics. In the classification phase, considering the instability of the recognition performance of the RKELM algorithm, the RKELM based on prototype clustering preprocessing is first proposed. Then, to improve the recognition performance, we proposed the feature level fusing RKELM and the decision level fusing RKELM to fuse the three projection components of the targets. The experiments compared the performance of the proposed recognition method and the state-of-the-art methods using the FP HRRP data from 10 civilian vehicles. The results demonstrate that the projection features by Cameron decomposition exhibit higher separability and better noise robustness, and that the feature level fusing RKELM has better generalization performance with a large number of training samples, but the decision level fusing RKELM was better with a small number of training samples.
Target Parameter Estimation
In the traditional coherent radar signal processing, the cascaded processing with pulse compression followed by coherent integration cannot achieve the maximum accumulation of high-speed target’s echo energy in theory. In addition, the result of the cascaded processing is characterized by deviation in the target peak position, accompanied by problems, such as the broadening of the main lobe, a decrease in the gain, and an increase in the side lobes. Therefore, this paper proposes a long time coherent integration method combining Pulse Compression and Radon-Fourier Transform (PC-RFT). This method utilizes the correlation between signals to combine matched filter and RFT. To maximize the target gain, the fast time (intra-pulse time) and slow time (inter-pulse time) dimensions are combined to compensate for the intra-pulse and inter-pulse Doppler shifts. The experimental results show that the two-dimensional joint processing outperforms the cascaded processing. In the traditional coherent radar signal processing, the cascaded processing with pulse compression followed by coherent integration cannot achieve the maximum accumulation of high-speed target’s echo energy in theory. In addition, the result of the cascaded processing is characterized by deviation in the target peak position, accompanied by problems, such as the broadening of the main lobe, a decrease in the gain, and an increase in the side lobes. Therefore, this paper proposes a long time coherent integration method combining Pulse Compression and Radon-Fourier Transform (PC-RFT). This method utilizes the correlation between signals to combine matched filter and RFT. To maximize the target gain, the fast time (intra-pulse time) and slow time (inter-pulse time) dimensions are combined to compensate for the intra-pulse and inter-pulse Doppler shifts. The experimental results show that the two-dimensional joint processing outperforms the cascaded processing.
The application of one-bit quantization technology in a massive MIMO radar system significantly reduced the system cost, power consumption, and transmission bandwidth. However, it also poses a severe challenge to extract high-precision target information from one-bit quantized data. To address the problem of low positioning accuracy and poor robustness of secondary positioning based on one-bit quantization under low Signal-to-Noise Ratio (SNR), this paper proposes a multi-station radar target direct position determination algorithm based on one-bit quantization. First, by quantizing the received signal with one bit, and deriving the probability distribution based on the one-bit signal, the cost function about the target position is established. Second, by proving the convexity of the cost function, the maximum likelihood estimation and gradient descent algorithm are used to solve the unknown signal parameters in the echo. Finally, the direct positioning of the target is achieved according to the maximum likelihood estimation. Simulation experiments were performed to analyze the positioning performance of the proposed algorithm, and the results showed that the proposed algorithm only needed to transmit 6.25% of the communication bandwidth compared with the high-bit sampling (e.g., 16 bits) direct position determination algorithm, and its power consumption is only 0.1% of the former. In addition, compared with the secondary positioning algorithm based on one-bit quantization, the proposed algorithm can achieve an effective estimation of the target position under a low SNR. In addition, its localization performance is significantly better than the former under low SNR and a low number of MIMO antennas. Simultaneously, its performance will be further improved with the application of oversampling technology. The application of one-bit quantization technology in a massive MIMO radar system significantly reduced the system cost, power consumption, and transmission bandwidth. However, it also poses a severe challenge to extract high-precision target information from one-bit quantized data. To address the problem of low positioning accuracy and poor robustness of secondary positioning based on one-bit quantization under low Signal-to-Noise Ratio (SNR), this paper proposes a multi-station radar target direct position determination algorithm based on one-bit quantization. First, by quantizing the received signal with one bit, and deriving the probability distribution based on the one-bit signal, the cost function about the target position is established. Second, by proving the convexity of the cost function, the maximum likelihood estimation and gradient descent algorithm are used to solve the unknown signal parameters in the echo. Finally, the direct positioning of the target is achieved according to the maximum likelihood estimation. Simulation experiments were performed to analyze the positioning performance of the proposed algorithm, and the results showed that the proposed algorithm only needed to transmit 6.25% of the communication bandwidth compared with the high-bit sampling (e.g., 16 bits) direct position determination algorithm, and its power consumption is only 0.1% of the former. In addition, compared with the secondary positioning algorithm based on one-bit quantization, the proposed algorithm can achieve an effective estimation of the target position under a low SNR. In addition, its localization performance is significantly better than the former under low SNR and a low number of MIMO antennas. Simultaneously, its performance will be further improved with the application of oversampling technology.