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24 pages, 9636 KiB

Open AccessArticle

Time-Range Adaptive Focusing Method Based on APC and Iterative Adaptive Radon-Fourier Transform

by Jian Guan, Jiazheng Pei, Yong Huang, Xiaolong Chen and Baoxin Chen

Remote Sens. 2022, 14(23), 6182; https://doi.org/10.3390/rs14236182 - 6 Dec 2022

Cited by 2 | Viewed by 1800

Abstract

In conventional radar signal processing, the cascade of pulse compression (i.e., matched filter) and Radon-Fourier transform (RFT) can extract the estimated scattering coefficient of the target in the range-velocity dimension through long-time coherent integration (i.e., long-time focusing). However, matched filter has problems such [...] Read more.

In conventional radar signal processing, the cascade of pulse compression (i.e., matched filter) and Radon-Fourier transform (RFT) can extract the estimated scattering coefficient of the target in the range-velocity dimension through long-time coherent integration (i.e., long-time focusing). However, matched filter has problems such as range sidelobes. RFT belongs to a standard time-dimension matched filter, which will cause velocity sidelobes of strong targets. The range-velocity sidelobes caused by matched filter and RFT will mask other weak targets and affect the subsequent signal processing processes such as target detection and tracking. To suppress range-velocity sidelobes and achieve better range-velocity focusing, this paper proposes a time-range adaptive focusing method named APC-IARFT for short, which is based on adaptive pulse compression (APC) and newly proposed iterative adaptive Radon-Fourier transform (IARFT). In the APC-IARFT method, the radar time-range adaptive focusing consists of two steps: range-dimension adaptive focusing and long-time adaptive focusing in the velocity dimension. The APC method can realize range-dimension adaptive focusing and suppress range sidelobes of strong targets. Then, based on the minimum variance distortionless response (MVDR) formulation, the proposed IARFT method iteratively designs time-dimension adaptive filter of each range-velocity grid according to the received signal processed by APC to suppress velocity sidelobes of strong targets and achieve long-time adaptive focusing. Compared with the conventional cascade of matched filter and RFT, the cascade of matched filter and adaptive Radon-Fourier transform (ARFT), the results show that the proposed time-range adaptive focusing method (i.e., APC-IARFT) is competent for a variety of scenarios. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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25 pages, 5938 KiB

Open AccessArticle

Multichannel Sea Clutter Measurement and Space-Time Characteristics Analysis with L-Band Shore-Based Radar

by Jintong Wan, Feng Luo, Yushi Zhang, Jinpeng Zhang and Xinyu Xu

Remote Sens. 2022, 14(21), 5312; https://doi.org/10.3390/rs14215312 - 24 Oct 2022

Cited by 2 | Viewed by 1723

Abstract

In order to study the space-time characteristics of sea clutter, the sea clutter is always measured by the airborne multichannel radar; however, the sea clutter shows the heterogeneity between range gates, which means the space-time covariance matrix’s correspondence to the single range gate [...] Read more.

In order to study the space-time characteristics of sea clutter, the sea clutter is always measured by the airborne multichannel radar; however, the sea clutter shows the heterogeneity between range gates, which means the space-time covariance matrix’s correspondence to the single range gate cannot be estimated accurately. Meanwhile, the measurement of the sea clutter data by the airborne radar is usually affected by the motion of the platform, which makes the analysis results unrepresentative of the space-time characteristics of the pure sea clutter. In this paper, a sea clutter measurement method based on L-band shore-based multichannel radar is proposed, where the transmit sub-array periodically moves with the pulse repetition period to obtain multiple sets of coherent processing interval pulses for each range gate. This measurement method can exclude the influences of the moving platform. Moreover, a sea clutter space-time signal model of the single range gate is proposed, and the model is used to simulate three-dimensional sea clutter data with space-time coupling characteristics. With verification of the measured and simulated data, it can be seen that the data composed of single range gate and multiple coherent processing interval pulses can accurately estimate the space-time covariance matrix corresponding to this single range gate. Furthermore, the space-time characteristics are analyzed based on the measured data. The results show that the eigenvalue spectrum and the spread width of space-time power spectrum are influenced by the backscattering coefficient of sea clutter and the speed of sea surface motion. In comparison, the decorrelation effect caused by the backscattering coefficient of sea clutter is stronger than that caused by the speed of the surface motion. The proposed method is helpful for guiding multichannel sea clutter measurement and the analysis results are of great significance to the clutter suppression algorithms of the marine multichannel radar. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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15 pages, 5942 KiB

Open AccessArticle

A Radar Detection Method of Plasma-Sheath-Covered Target Based on the Improved Keystone Algorithm

by Bowen Bai, Yi Ding, Xiaoping Li and Yanming Liu

Remote Sens. 2022, 14(19), 4869; https://doi.org/10.3390/rs14194869 - 29 Sep 2022

Cited by 3 | Viewed by 1724

Abstract

The aerodynamic thermal ionization affects the re-entry target, and the surface will form a ‘plasma sheath (PSh).’ The PSh with fluid characteristics will produce relative motion with the re-entry target. In the radar detection of the re-entry target, the relative motion characteristics cause [...] Read more.

The aerodynamic thermal ionization affects the re-entry target, and the surface will form a ‘plasma sheath (PSh).’ The PSh with fluid characteristics will produce relative motion with the re-entry target. In the radar detection of the re-entry target, the relative motion characteristics cause the echo signal to couple different intra-pulse Doppler frequency components, forming a ‘false target’ on the one-dimensional range profile. In addition, the flight velocity of the re-entry target is exceptionally high (usually greater than 10 Mach), and there will be a severe phenomenon of migration through range cells (MTRC) during the detection period, which will make the coherent integration of the multi-period radar echo signal invalid and further affect the reliable detection of the re-entry target. Aiming at the ‘false target phenomenon’ and MTRC phenomenon in the process of re-entry target detection, this paper proposes an improved keystone algorithm. Based on the traditional keystone algorithm, a reliable, coherent integration method for radar echo of the plasma-sheath-covered target is proposed by modifying the scale transformation factor and constructing the Doppler frequency compensation function. It can effectively compensate the intra-pulse Doppler frequency and inter-pulse Doppler frequency to improve the energy gain of the real target and lay a theoretical foundation for the reliable detection of the plasma-sheath-covered target. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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21 pages, 530 KiB

Open AccessArticle

Sparsity-Based Two-Dimensional DOA Estimation for Co-Prime Planar Array via Enhanced Matrix Completion

by Donghe Liu, Yongbo Zhao and Tingxiao Zhang

Remote Sens. 2022, 14(19), 4690; https://doi.org/10.3390/rs14194690 - 20 Sep 2022

Cited by 5 | Viewed by 1849

Abstract

In this paper, the two-dimensional (2-D) direction-of-arrival (DOA) estimation problem is explored for the sum-difference co-array (SDCA) generated by the virtual aperture expansion of co-prime planar arrays (CPPA). Since the SDCA has holes, this usually causes the maximum virtual aperture of CPPA to [...] Read more.

In this paper, the two-dimensional (2-D) direction-of-arrival (DOA) estimation problem is explored for the sum-difference co-array (SDCA) generated by the virtual aperture expansion of co-prime planar arrays (CPPA). Since the SDCA has holes, this usually causes the maximum virtual aperture of CPPA to be unavailable. To address this issue, we propose a complex-valued, sparse matrix recovery-based 2-D DOA estimation algorithm for CPPA via enhanced matrix completion. First, we extract the difference co-arrays (DCA) from SDCA and construct the co-array interpolation model via nuclear norm minimization to initialize the virtual uniform rectangular array (URA) that does not contain the entire rows and columns of holes. Then, we utilize the shift-invariance structure of the virtual URA to construct the enhanced matrix with a two-fold Hankel structure to fill the remaining empty elements. More importantly, we apply the alternating direction method of the multipliers (ADMM) framework to solve the enhanced matrix completion model. To reduce the computational complexity of the traditional vector-form, sparse recovery algorithm caused by the Kronecker product operation between dictionary matrices, we derive a complex-valued sparse matrix-recovery model based on the fast iterative shrinkage-thresholding (FISTA) method. Finally, simulation results demonstrate the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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19 pages, 2448 KiB

Open AccessArticle

Maneuvering Extended Object Tracking with Modified Star-Convex Random Hypersurface Model Based on Minimum Cosine Distance

by Lifan Sun, Jinjin Zhang, Haofang Yu, Zhumu Fu and Zishu He

Remote Sens. 2022, 14(17), 4376; https://doi.org/10.3390/rs14174376 - 3 Sep 2022

Cited by 8 | Viewed by 2049

Abstract

Maneuvering extended object tracking is a new research field due to the rapid development of modern sensor technology. Multiple measurements may be resolved from different unknown sources on an object by using a high-resolution radar. In this case, the object should be regarded [...] Read more.

Maneuvering extended object tracking is a new research field due to the rapid development of modern sensor technology. Multiple measurements may be resolved from different unknown sources on an object by using a high-resolution radar. In this case, the object should be regarded as an extended one with object extension, e.g., its shape may be described by the star-convex random hypersurface model. This model is usually specified by a one-dimensional radial function. However, the divergence of the shape estimation and a high error of the kinematic state estimation are likely to occur when an extended object maneuvers. This is because the radial function may take a negative value after Fourier series expansion, which leads to unpredictable estimation results. Unfortunately, the model itself is unable to solve this problem via the subsequent iterations. In this paper, we proposed a modified shape estimation approach to track an extended object with a star-convex random hypersurface model based on minimum cosine distance. Both the extension state and kinematic state at the current time are reinitialized once the radial function takes a negative value. Moreover, a mathematical model was constructed by using the principle of minimum cosine distance, so as to obtain more reasonable weight distribution coefficients for the correction of the extension state. Simulation results in different scenarios demonstrated the effectiveness of the proposed tracking approach. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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18 pages, 4619 KiB

Open AccessArticle

Airborne Passive Bistatic Radar Clutter Suppression Algorithm Based on Root Off-Grid Sparse Bayesian Learning

by Jipeng Wang, Jun Wang, Luo Zuo, Shuai Guo and Dawei Zhao

Remote Sens. 2022, 14(16), 3963; https://doi.org/10.3390/rs14163963 - 15 Aug 2022

Viewed by 2118

Abstract

When the transmitter is in motion, the airborne passive bistatic radar (PBR) has a complex clutter geometry and lacks independent and identically distributed training samples in clutter estimation and suppression. In order to solve these problems, this paper proposes a space–time adaptive processing [...] Read more.

When the transmitter is in motion, the airborne passive bistatic radar (PBR) has a complex clutter geometry and lacks independent and identically distributed training samples in clutter estimation and suppression. In order to solve these problems, this paper proposes a space–time adaptive processing (STAP) algorithm based on root off-grid sparse Bayesian learning. The proposed algorithm first models the space–time base of the dictionary as an adjustable state. Then, the positions of those dynamic bases are optimized by iterating a maximum expectation algorithm. In this way, the off-grid error in clutter estimation can be eliminated even when the modeling grid is wide. To further improve the accuracy of clutter estimation, the proposed algorithm eliminates the error caused by samples with singular values in the root off-grid sparse Bayes learning by artificially adding pseudorandom noise and using hypothesis testing. The simulation results show that the proposed algorithm achieves better performance than the existing algorithms. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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24 pages, 1128 KiB

Open AccessArticle

Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder

by Chenxi Zhang, Huiliang Zhao, Wenchao Chen, Bo Chen, Penghui Wang, Changrui Jia and Hongwei Liu

Remote Sens. 2022, 14(15), 3800; https://doi.org/10.3390/rs14153800 - 6 Aug 2022

Cited by 3 | Viewed by 1983

Abstract

Due to the shortage of independent and identically distributed (i.i.d.) training samples, space−time adaptive processing (STAP) often suffers remarkable performance degradation in the heterogeneous clutter environment. Sparse recovery (SR) techniques have been introduced into STAP for the benefit of the drastically reduced training [...] Read more.

Due to the shortage of independent and identically distributed (i.i.d.) training samples, space−time adaptive processing (STAP) often suffers remarkable performance degradation in the heterogeneous clutter environment. Sparse recovery (SR) techniques have been introduced into STAP for the benefit of the drastically reduced training requirement, but they are incompletely robust for involving the tricky selection of hyper−parameters or the undesirable point estimation for parameters. Given this issue, we incorporate the Multiple−measurement Complex−valued Variational relevance vector machines (MCV) to model the space−time echoes and provide a Gibbs−sampling−based method to estimate posterior distributions of parameters accurately. However, the Gibbs sampler require quantities of iterations, as unattractive as traditional Bayesian type SR−STAP algorithms when the real−time processing is desired. To address this problem, we further develop the Bayesian Autoencoding MCV for STAP (BAMCV−STAP), which builds the generative model according to MCV and approximates posterior distributions of parameters with an inference network pre−trained off−line, to realize fast reconstruction of measurements. Experimental results on simulated and measured data demonstrate that BAMCV−STAP can achieve suboptimal clutter suppression in terms of the output signal to interference plus noise ratio (SINR) loss, as well as the attractive real−time processing property in terms of the convergence rate and computational loads. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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18 pages, 5755 KiB

Open AccessArticle

Influence of Plasma Sheath’s Velocity Field on ISAR Imaging of Hypersonic Target

by Yaocong Xie, Xiaoping Li, Fangfang Shen, Zheng Mao, Bowen Bai and Xuyang Chen

Remote Sens. 2022, 14(15), 3799; https://doi.org/10.3390/rs14153799 - 6 Aug 2022

Cited by 2 | Viewed by 1911

Abstract

Plasma sheath poses a serious challenge to inverse synthetic aperture radar (ISAR) imaging of hypersonic targets. This paper investigated the distribution characteristics of the electron density and velocity field in the plasma sheath surrounding the hypersonic target in various flight scenes. The incident [...] Read more.

Plasma sheath poses a serious challenge to inverse synthetic aperture radar (ISAR) imaging of hypersonic targets. This paper investigated the distribution characteristics of the electron density and velocity field in the plasma sheath surrounding the hypersonic target in various flight scenes. The incident depth and reflective surface of electromagnetic (EM) waves with X-band, Ku-band, and Ka-band can be determined based on the plasma frequency. We established the echo model coupled with the velocity field of the plasma sheath on the reflective surface and obtained one-dimensional range profiles and ISAR images of the hypersonic target in various flight scenes. The simulation results indicated that the non-uniform velocity field on the reflective surface induced displacement and diffusion in the one-dimensional range profile, resulting in ISAR image distortion. A changing flight scene and radar frequency can have an impact on imaging results. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) were utilized to assess the impact of plasma sheath on ISAR images. This study revealed the defocus mechanism of the ISAR image caused by the velocity field of the plasma sheath and provided a theoretical reference for the selection of radar frequency for hypersonic targets in various flight scenes. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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24 pages, 10465 KiB

Open AccessArticle

Experimental Study of Maritime Moving Target Detection Using Hitchhiking Bistatic Radar

by Jie Song, Wei Xiong, Xiaolong Chen and Yuan Lu

Remote Sens. 2022, 14(15), 3611; https://doi.org/10.3390/rs14153611 - 28 Jul 2022

Cited by 8 | Viewed by 2502

Abstract

Hitchhiking bistatic radar system takes the direct wave signal that is transmitted by the non-cooperative radar emitter as the reference to detect and analyze the target echo signal, so as to realize the positioning and tracking of the target. This radar system has [...] Read more.

Hitchhiking bistatic radar system takes the direct wave signal that is transmitted by the non-cooperative radar emitter as the reference to detect and analyze the target echo signal, so as to realize the positioning and tracking of the target. This radar system has the advantages of low cost and strong survivability. Aiming at the problem of passive radar to covert the detection of maritime targets, this paper develops a hitchhiking bistatic radar system for maritime target detection, which uses the shore-based radar as the non-cooperative radar emitter. By continuously collecting the direct wave and target echo data of the non-cooperative radar, the direct wave reference signal reconstruction, pulse compression, interference suppression and synchronization processing, non-coherent integration, MTI (moving target indication), clutter map processing, and adaptive CFAR (constant false alarm rate) detection are completed to obtain the azimuth, bistatic range, and Doppler frequency of the target, and finally realize the positioning of non-cooperative maritime targets. This paper first introduces and demonstrates the composition principle of the system, introduces the signal processing implementation method of the system in detail, and tests and analyzes the key algorithms. The experimental results show that the system can realize the passive coherent detection of maritime moving targets and locate multiple targets at the same time. The experiment obtains a very clear PPI (plane position indicator) display picture of the hitchhiking bistatic radar system, and the radar detection data of the experimental system is in good agreement with the AIS (automatic identification system) data. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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23 pages, 7146 KiB

Open AccessArticle

Change Detection Based on Fusion Difference Image and Multi-Scale Morphological Reconstruction for SAR Images

by Jiayu Xuan, Zhihui Xin, Guisheng Liao, Penghui Huang, Zhixu Wang and Yu Sun

Remote Sens. 2022, 14(15), 3604; https://doi.org/10.3390/rs14153604 - 27 Jul 2022

Cited by 7 | Viewed by 2300

Abstract

Synthetic aperture radar (SAR) image-change detection is widely used in various fields, such as environmental monitoring and ecological monitoring. There is too much noise and insufficient information utilization, which make the results of change detection inaccurate. Thus, we propose an SAR image-change-detection method [...] Read more.

Synthetic aperture radar (SAR) image-change detection is widely used in various fields, such as environmental monitoring and ecological monitoring. There is too much noise and insufficient information utilization, which make the results of change detection inaccurate. Thus, we propose an SAR image-change-detection method based on multiplicative fusion difference image (DI), saliency detection (SD), multi-scale morphological reconstruction (MSMR), and fuzzy c-means (FCM) clustering. Firstly, a new fusion DI method is proposed by multiplying the ratio (R) method based on the ratio of the image before and after the change and the mean ratio (MR) method based on the ratio of the image neighborhood mean value. The new DI operator ratio–mean ratio (RMR) enlarges the characteristics of unchanged areas and changed areas. Secondly, saliency detection is used in DI, which is conducive to the subsequent sub-area processing. Thirdly, we propose an improved FCM clustering-change-detection method based on MSMR. The proposed method has high computational efficiency, and the neighborhood information obtained by morphological reconstruction is fully used. Six real SAR data sets are used in different experiments to demonstrate the effectiveness of the proposed saliency ratio–mean ratio with multi-scale morphological reconstruction fuzzy c-means (SRMR-MSMRFCM). Finally, four classical noise-sensitive methods are used to detect our DI method and demonstrate the strong denoising and detail-preserving ability. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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19 pages, 7138 KiB

Open AccessArticle

A Variable-Scale Coherent Integration Method for Moving Target Detection in Wideband Radar

by Tingkun Lu, Feng He, Lei Yu and Manqing Wu

Remote Sens. 2022, 14(13), 3156; https://doi.org/10.3390/rs14133156 - 1 Jul 2022

Cited by 1 | Viewed by 1942

Abstract

Accurate integration of the extended target’s energy is one of the important challenges of moving target detection in wideband radar. In this paper, a coherent integration method for wideband radar, i.e., variable-scale moving target detection (VSMTD), is proposed to resist range migration and [...] Read more.

Accurate integration of the extended target’s energy is one of the important challenges of moving target detection in wideband radar. In this paper, a coherent integration method for wideband radar, i.e., variable-scale moving target detection (VSMTD), is proposed to resist range migration and Doppler broadening. On the one hand, subband decomposition can effectively integrate the energy of the extended target in range using variable-scale transformation, accomplished by modulating the filter bank. On the other hand, it increases the coherent integration time by mitigating the range migration in a sufficiently narrow subband. The discrete Fourier transform (DFT) modulated filter bank and the fast Fourier transform (FFT) algorithm are also used to achieve fast VSMTD implementation. Finally, the simulation results demonstrate the superior performance of the proposed VSMTD method. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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21 pages, 6451 KiB

Open AccessArticle

Online Sequential Extreme Learning Machine-Based Active Interference Activity Prediction for Cognitive Radar

by Shanshan Wang, Zheng Liu, Rong Xie and Lei Ran

Remote Sens. 2022, 14(12), 2737; https://doi.org/10.3390/rs14122737 - 7 Jun 2022

Cited by 1 | Viewed by 1941

Abstract

For anti-active-interference-oriented cognitive radar systems, the mismatch between the acquired and actual interference information may result in serious degradation of cognitive anti-active-interference performance. To yield more effective knowledge of the electromagnetic environment and eliminate the mismatch effect, the electromagnetic activity prediction technique, which [...] Read more.

For anti-active-interference-oriented cognitive radar systems, the mismatch between the acquired and actual interference information may result in serious degradation of cognitive anti-active-interference performance. To yield more effective knowledge of the electromagnetic environment and eliminate the mismatch effect, the electromagnetic activity prediction technique, which deduces future electromagnetic behaviors based on current observations, has received increasing attention. However, high computational complexities limit the application of conventional electromagnetic activity prediction methods in dynamic active interference prediction with high real-time requirements. In this paper, the online sequential extreme learning machine (OS-ELM)-based method, which is dedicated to high-efficiency active interference activity prediction, is proposed. The advancement includes two aspects. First, benefiting from the single-hidden-layer network structure and recursive-formula-based output weight updating, the proposed OS-ELM-based frequency prediction (OS-ELM-FP) and OS-ELM-based angle prediction (OS-ELM-AP) models can predict the interference state and update the prediction model parameters with much higher computational efficiency. Second, the better generalization performance enables the proposed method to achieve smaller interference activity prediction errors compared with conventional methods. Numerical examples and prediction results based on measured jamming data demonstrate the advantages of the proposed method. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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20 pages, 5471 KiB

Open AccessArticle

A Coherent Integration Segment Searching Based GRT-GRFT Hybrid Integration Method for Arbitrary Fluctuating Target

by Zhenghe Zhang, Nan Liu, Yongning Hou, Shiyu Zhang and Linrang Zhang

Remote Sens. 2022, 14(11), 2695; https://doi.org/10.3390/rs14112695 - 3 Jun 2022

Cited by 7 | Viewed by 2166

Abstract

Long-time integration is an effective method for improving the signal–to–noise ratio (SNR) of an echo. However, if the target radar cross-section (RCS) fluctuates over the long integration time, the traditional coherent integration and noncoherent integration methods will produce significant performance losses, making it [...] Read more.

Long-time integration is an effective method for improving the signal–to–noise ratio (SNR) of an echo. However, if the target radar cross-section (RCS) fluctuates over the long integration time, the traditional coherent integration and noncoherent integration methods will produce significant performance losses, making it impossible to achieve a favorable integration performance at low SNRs. This study proposes a new hybrid integration method based on the generalized Radon–Fourier transform (GRFT) and generalized Radon transform (GRT) for targets with which echoes are partially coherent. First, a coherent integration is performed with GRFT within the optimal coherent processing segment using optimal coherent processing segmented matching. Then, the GRT is used for noncoherent integration between the coherent processing sections, and the target motion parameters are obtained through a global search. Compared with the GRFT, GRT, and moving target detection (MTD)-GRT methods, the proposed method applies to targets with arbitrary RCS fluctuations, arbitrary cross-range cells, and cross-Doppler cells, and offers the best detection performance. Finally, both simulation results and measured data processing results demonstrate the effectiveness of the algorithm. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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21 pages, 8911 KiB

Open AccessArticle

Micro-Doppler Parameters Extraction of Precession Cone-Shaped Targets Based on Rotating Antenna

by Zhihao Wang, Ying Luo, Kaiming Li, Hang Yuan and Qun Zhang

Remote Sens. 2022, 14(11), 2549; https://doi.org/10.3390/rs14112549 - 26 May 2022

Cited by 14 | Viewed by 2516

Abstract

Micro-Doppler is regarded as a unique signature of a target with micro-motions. The sophisticated recognition of the cone-shaped targets can be realized through the micro-Doppler effect. However, it is difficult to extract the micro-motion features perpendicular to the radar line of sight (LOS) [...] Read more.

Micro-Doppler is regarded as a unique signature of a target with micro-motions. The sophisticated recognition of the cone-shaped targets can be realized through the micro-Doppler effect. However, it is difficult to extract the micro-motion features perpendicular to the radar line of sight (LOS) effectively. In this paper, a micro-Doppler parameters extraction method of the cone-shaped targets is put forward based on the rotating antenna. First, a new radar configuration is proposed, in which an antenna rotates uniformly on a fixed circle, thus producing Doppler frequency shift. Second, the expression of the micro-Doppler frequency shift induced by the precession cone-shaped target is derived. Then, the micro-Doppler curves of point scatterers at the cone top and bottom are separated by the smoothness of the curves, and the empirical mode decomposition (EMD) method is utilized for the detection and estimation of the coning frequency. Finally, the micro-motion components perpendicular to the radar LOS are inverted by the peak of micro-Doppler frequency curve. Simulation results prove the effectiveness and robustness of the proposed method. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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21 pages, 3936 KiB

Open AccessArticle

Beamspace Scene Classification Algorithm for Low-Angle Estimation in MIMO Radar

by Sheng Chen, Yongbo Zhao, Yili Hu and Ben Niu

Remote Sens. 2022, 14(8), 1917; https://doi.org/10.3390/rs14081917 - 15 Apr 2022

Cited by 1 | Viewed by 1613

Abstract

This paper discusses the issue of low-angle estimation in multiple-input, multiple-output (MIMO) radar. Due to the low amount of data transmission, storage, and computation required for beamspace super-resolution algorithms in low-angle estimation, the method has gained considerable interest in recent years. This paper [...] Read more.

This paper discusses the issue of low-angle estimation in multiple-input, multiple-output (MIMO) radar. Due to the low amount of data transmission, storage, and computation required for beamspace super-resolution algorithms in low-angle estimation, the method has gained considerable interest in recent years. This paper develops a beamspace scene classification (BSC) algorithm to enhance the performance of low-angle estimation in MIMO radar. The proposed BSC algorithm solves an initial angle estimate and the multipath coefficient estimate using the 3D beamspace data, and further constructs the beamspace data required for transmitting and receiving sides to reduce the data dimensions. It is additionally used to provide three estimation schemes with closed-form solutions for three multipath scenes (obtaining two estimations for transmitting and receiving sides). Finally, it fuses the estimates of the two sides by the minimum variance criterion. As a result, the proposed method achieves high estimation accuracy while requiring few processing resources. Moreover, the computational complexity of the proposed algorithm is examined in this study, with the results demonstrating that the proposed method is superior for engineering applications. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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16 pages, 4724 KiB

Open AccessArticle

Parameter Estimation for Precession Cone-Shaped Targets Based on Range–Frequency–Time Radar Data Cube

by Lixun Han and Cunqian Feng

Remote Sens. 2022, 14(7), 1548; https://doi.org/10.3390/rs14071548 - 23 Mar 2022

Cited by 7 | Viewed by 2302

Abstract

A radar echo signal received from a cone-shaped target with precession contains micro-Doppler (m-D) information from different effective scattering centers. By taking full advantage of the m-D information, this paper proposes a parameter estimation algorithm for precession cone-shaped targets based on the range–frequency–time [...] Read more.

A radar echo signal received from a cone-shaped target with precession contains micro-Doppler (m-D) information from different effective scattering centers. By taking full advantage of the m-D information, this paper proposes a parameter estimation algorithm for precession cone-shaped targets based on the range–frequency–time radar data cube (RDC). We build scattering center models of precession cone-shaped targets with the occlusion effect. The Binary Mask method is first utilized to obtain a high-resolution range-Doppler (RD) sequence. On this basis, the range–frequency–time RDC can be extracted from the RD sequence. In order to approach the actual case, we discuss the parameter estimation algorithm under different radar lines-of-sight (LOS). The most attractive attribute of this algorithm is that it can conduct in-depth research on m-D parameter estimation from a three-dimensional (3D) domain. Finally, the experimental results illustrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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20 pages, 3608 KiB

Open AccessArticle

Ground Maneuvering Target Focusing via High-Order Phase Correction in High-Squint Synthetic Aperture Radar

by Lei Ran, Zheng Liu and Rong Xie

Remote Sens. 2022, 14(6), 1514; https://doi.org/10.3390/rs14061514 - 21 Mar 2022

Cited by 4 | Viewed by 2299

Abstract

Moving target imaging in high-squint synthetic aperture radar (SAR) shows great potential for reconnaissance and surveillance tasks. For the desired resolution, high-squint SAR has a long-time coherent processing interval (CPI). In this case, the maneuvering motion of the moving target usually causes high-order [...] Read more.

Moving target imaging in high-squint synthetic aperture radar (SAR) shows great potential for reconnaissance and surveillance tasks. For the desired resolution, high-squint SAR has a long-time coherent processing interval (CPI). In this case, the maneuvering motion of the moving target usually causes high-order phase terms in the echoed data, which cannot be neglected for precise focusing. Many ground moving target imaging (GMTIm) algorithms have been proposed in the literature, but some high-order phase terms remain uncompensated in high-squint SAR. For this problem, a high-order phase correction-based GMTIm (HPC-GMTIm) method is proposed in this paper. We assumed that the target of interest has a constant velocity in the subaperture CPI, but maneuvering motion parameters for the whole CPI. Within the short subaperture CPI, the target signal can be simplified as a three-order phase expression, and the instantaneous Doppler frequency (DF) was estimated by some time–frequency analysis tools, including the Hough transform and the fractional Fourier transform. For the whole CPI, the subaperture, the instantaneous DF was combined to form a total least-squares problem, outputting the undetermined phase coefficients. Using the proposed local-to-global processing chain, all high-order phase terms can be estimated and corrected, which outperforms existing methods. The effectiveness of the HPC-GMTIm method is demonstrated by real measured high-squint SAR data. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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25 pages, 13361 KiB

Open AccessArticle

Micro-Motion Classification of Flying Bird and Rotor Drones via Data Augmentation and Modified Multi-Scale CNN

by Xiaolong Chen, Hai Zhang, Jie Song, Jian Guan, Jiefang Li and Ziwen He

Remote Sens. 2022, 14(5), 1107; https://doi.org/10.3390/rs14051107 - 24 Feb 2022

Cited by 22 | Viewed by 3742

Abstract

Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC [...] Read more.

Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target’s m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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24 pages, 11932 KiB

Open AccessArticle

Target Detection and DOA Estimation for Passive Bistatic Radar in the Presence of Residual Interference

by Haitao Wang, Jun Wang, Junzheng Jiang, Kefei Liao and Ningbo Xie

Remote Sens. 2022, 14(4), 1044; https://doi.org/10.3390/rs14041044 - 21 Feb 2022

Cited by 6 | Viewed by 2953

Abstract

With the development of radio technology, passive bistatic radar (PBR) will suffer from interferences not only from the base station that is used as the illuminator of opportunity (BS-IoO), but also from the base station with co-frequency or adjacent frequency (BS-CF/AF). It is [...] Read more.

With the development of radio technology, passive bistatic radar (PBR) will suffer from interferences not only from the base station that is used as the illuminator of opportunity (BS-IoO), but also from the base station with co-frequency or adjacent frequency (BS-CF/AF). It is difficult for clutter cancellation algorithm to suppress all the interferences, especially the interferences from BS-CF/AF. The residual interferences will seriously affect target detection and DOA estimation. To solve this problem, a novel target detection and DOA estimation method for PBR based on compressed sensing sparse reconstruction is proposed. Firstly, clutter cancellation algorithm is used to suppress the interferences from BS-IoO. Secondly, the residual interferences and target echo are separated in spatial domain based on the azimuth sparse reconstruction. Finally, target detection and DOA estimation method are given. The proposed method can achieve not only target detection and DOA estimation in the presence of residual interferences, but also better anti-mainlobe interferences and high-resolution DOA estimation performance. Numerical simulation and experimental results verify the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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25 pages, 4807 KiB

Open AccessArticle

Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform

by Bin Rao, Yongkun Zhou and Yuanping Nie

Remote Sens. 2022, 14(3), 491; https://doi.org/10.3390/rs14030491 - 20 Jan 2022

Cited by 5 | Viewed by 2312

Abstract

An elliptical Hough transform (EHT) algorithm is proposed in the framework of track-before-detect (TBD) for joint detection and tracking of weak exoatmospheric targets. The new approach exploits the fact that when restricted to a two-body problem, the exoatmospheric target often follows an elliptical [...] Read more.

An elliptical Hough transform (EHT) algorithm is proposed in the framework of track-before-detect (TBD) for joint detection and tracking of weak exoatmospheric targets. The new approach exploits the fact that when restricted to a two-body problem, the exoatmospheric target often follows an elliptical orbit, and thus the Hough transform integrated with orbital geometry information would have better detection performance. The relationship between the original radar measurements in data space and the elliptical parameters in parameter space is explicitly derived with multiple steps of coordinate transformation. It is found that the data points mapping into the parameter space essentially represent a quartic curve. An EHT-based algorithm is then designed, and orbit planarity is also taken into account to reduce the effect of noise accumulation. The influences of primary and secondary thresholds and the signal-to-noise ratio (SNR) on the detection performance are compared by simulations. Additionally, a real radar tracking dataset from a scientific satellite on 28 May 2017 is used to investigate the efficiency of the method. By adding some imaginary clutter to the raw orbit, the results indicate that it is very effective in detecting the real satellite trajectory in a low signal-to-noise ratio (SNR) environment. The advantage of the new method lies in it can not only simultaneously detect and track weak exoatmospheric targets but also can predict the trajectory by using these available detected parameters. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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24 pages, 5933 KiB

Open AccessArticle

GMT-WGAN: An Adversarial Sample Expansion Method for Ground Moving Targets Classification

by Xin Yao, Xiaoran Shi, Yaxin Li, Li Wang, Han Wang, Shijie Ren and Feng Zhou

Remote Sens. 2022, 14(1), 123; https://doi.org/10.3390/rs14010123 - 28 Dec 2021

Cited by 2 | Viewed by 2338

Abstract

In the field of target classification, detecting a ground moving target that is easily covered in clutter has been a challenge. In addition, traditional feature extraction techniques and classification methods usually rely on strong subjective factors and prior knowledge, which affect their generalization [...] Read more.

In the field of target classification, detecting a ground moving target that is easily covered in clutter has been a challenge. In addition, traditional feature extraction techniques and classification methods usually rely on strong subjective factors and prior knowledge, which affect their generalization capacity. Most existing deep-learning-based methods suffer from insufficient feature learning due to the lack of data samples, which makes it difficult for the training process to converge to a steady-state. To overcome these limitations, this paper proposes a Wasserstein generative adversarial network (WGAN) sample enhancement method for ground moving target classification (GMT-WGAN). First, the micro-Doppler characteristics of ground moving targets are analyzed. Next, a WGAN is constructed to generate effective time–frequency images of ground moving targets and thereby enrich the sample database used to train the classification network. Then, image quality evaluation indexes are introduced to evaluate the generated spectrogram samples, with an aim to verify the distribution similarity of generated and real samples. Afterward, by feeding augmented samples to the deep convolutional neural networks with good generalization capacity, the classification performance of the GMT-WGAN is improved. Finally, experiments conducted on different datasets validate the effectiveness and robustness of the proposed method. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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19 pages, 779 KiB

Open AccessTechnical Note

Joint Angle and Range Estimation in Monostatic FDA-MIMO Radar via Compressed Unitary PARAFAC

by Wenshuai Wang, Xianpeng Wang, Jinmei Shi and Xiang Lan

Remote Sens. 2022, 14(6), 1398; https://doi.org/10.3390/rs14061398 - 14 Mar 2022

Cited by 3 | Viewed by 2064

Abstract

In this paper, we study the joint range and angle estimation problem based in monostatic frequency diverse-array multiple-input multiple-output (FDA-MIMO) radar, and propose a method for range and angle estimation base on compressed unitary parallel factor (PARAFAC). First, the received complex signal matrix [...] Read more.

In this paper, we study the joint range and angle estimation problem based in monostatic frequency diverse-array multiple-input multiple-output (FDA-MIMO) radar, and propose a method for range and angle estimation base on compressed unitary parallel factor (PARAFAC). First, the received complex signal matrix is stacked into a third-order complex signal tensor. Then, we can transform the obtained third-order complex signal tensor into a third-order real-valued signal tensor by employing forward–backward and unitary transformation techniques. Next, a smaller third-order real-valued signal tensor is composed by using compressing the third-order real-valued signal tensor. After that, PARAFAC decomposition is applied to obtain the direction matrix. Lastly, the angle and range are estimated by employing the least square (LS) fitting. The estimation error of the proposed method is about

10 %

lower than that of the traditional PARAFAC method under the low number of snapshots. When the number of snapshots is high, the performance of the two methods is close. Moreover, the computational complexity of the proposed method is nearly

96 %

less than those of the traditional PARAFAC methods in the case of low snapshots, while the gap is larger in the case of high snapshots. The superiority and effectiveness of the method are proved by complexity analysis and simulation experiments. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)

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