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23 pages, 7234 KiB

Open AccessArticle

Attention-Enhanced Dual-Branch Residual Network with Adaptive L-Softmax Loss for Specific Emitter Identification under Low-Signal-to-Noise Ratio Conditions

by Zehuan Jing, Peng Li, Bin Wu, Erxing Yan, Yingchao Chen and Youbing Gao

Remote Sens. 2024, 16(8), 1332; https://doi.org/10.3390/rs16081332 - 10 Apr 2024

Viewed by 248

Abstract

To address the issue associated with poor accuracy rates for specific emitter identification (SEI) under low signal-to-noise ratio (SNR) conditions, where the single-dimension radar signal characteristics are severely affected by noise, we propose an attention-enhanced dual-branch residual network structure based on the adaptive [...] Read more.

To address the issue associated with poor accuracy rates for specific emitter identification (SEI) under low signal-to-noise ratio (SNR) conditions, where the single-dimension radar signal characteristics are severely affected by noise, we propose an attention-enhanced dual-branch residual network structure based on the adaptive large-margin Softmax (ALS). Initially, we designed a dual-branch network structure to extract features from one-dimensional intermediate frequency data and two-dimensional time–frequency images, respectively. By assigning different attention weights according to their importance, these features are fused into an enhanced joint feature for further training. This approach enables the model to extract distinctive features across multiple dimensions and achieve good recognition performance even when the signal is affected by noise. In addition, we have introduced L-Softmax to replace the original Softmax and propose the ALS. This approach adaptively calculates the classification margin decision parameter based on the angle between samples and the classification boundary and adjusts the margin values of the sample classification boundaries; it reduces the intra-class distance for the same class while increasing the inter-class distance between different classes without the need for cumbersome experiments to determine the optimal value of decision parameters. Our experimental findings revealed that, in comparison to alternative methods, our proposed approach markedly enhances the model’s capability to extract features from signals and classify them in low-SNR environments, thereby effectively diminishing the influence of noise. Notably, it achieves the highest recognition rate across a range of low-SNR conditions, registering an average increase in recognition rate of 4.8%. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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23 pages, 7546 KiB

Open AccessArticle

Symmetric Double-Supplemented Nested Array for Passive Localization of Mixed Near-Field and Far-Field Sources

by Yichen Wu, Junwei Qi, Ying-Zhen Wang and Yingsong Li

Remote Sens. 2024, 16(6), 1027; https://doi.org/10.3390/rs16061027 - 14 Mar 2024

Viewed by 445

Abstract

In mixed-field source localization, the physical properties of a sensor array, such as the degrees of freedom (DOFs), aperture, and coupling leakage, directly affect the accuracy of estimating the direction of arrival (DOA). Compared to conventional symmetric uniform linear arrays, symmetric non-uniform linear [...] Read more.

In mixed-field source localization, the physical properties of a sensor array, such as the degrees of freedom (DOFs), aperture, and coupling leakage, directly affect the accuracy of estimating the direction of arrival (DOA). Compared to conventional symmetric uniform linear arrays, symmetric non-uniform linear arrays (SNLAs) have a greater advantage in mixed-field source localization due to their larger aperture and higher DOF. However, current SNLAs require improvements in their physical properties through modifications to the array structure in order to achieve more accurate source localization estimates. Therefore, this study proposes a symmetric double-supplemented nested array (SDSNA), which translates nested subarrays based on symmetric nested arrays to increase the aperture and inserts two symmetric supplemented subarrays to fill the holes created by the translation. This method results in longer consecutive difference coarray lags and larger apertures. The SDSNA is compared to existing advanced SNLAs in terms of their physical properties and DOA estimation. The results show that, with the same number of sensors, the SDSNA has a higher DOF, a larger aperture, and smaller coupling, indicating the advantages of the SDSNA in terms of its physical properties. Under the same experimental conditions, the SDSNA has a lower root-mean-square error of source location, thus indicating better performance in terms of both DOA and distance estimation. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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29 pages, 24530 KiB

Open AccessArticle

An Off-Grid Compressive Sensing Algorithm Based on Sparse Bayesian Learning for RFPA Radar

by Ju Wang, Bingqi Shan, Song Duan, Yi Zhao and Yi Zhong

Remote Sens. 2024, 16(2), 403; https://doi.org/10.3390/rs16020403 - 20 Jan 2024

Viewed by 593

Abstract

In the application of Compressive Sensing (CS) theory for sidelobe suppression in Random Frequency and Pulse Repetition Interval Agile (RFPA) radar, the off−grid issues affect the performance of target parameter estimation in RFPA radar. Therefore, to address this issue, this paper presents an [...] Read more.

In the application of Compressive Sensing (CS) theory for sidelobe suppression in Random Frequency and Pulse Repetition Interval Agile (RFPA) radar, the off−grid issues affect the performance of target parameter estimation in RFPA radar. Therefore, to address this issue, this paper presents an off−grid CS algorithm named Refinement and Generalized Double Pareto (GDP) distribution based on Sparse Bayesian Learning (RGDP−SBL) for RFPA radar that utilizes a coarse−to−fine grid refinement approach, allowing precise and cost−effective signal recovery while mitigating the impact of off−grid issues on target parameter estimation. To obtain a high-precision signal recovery, especially in scenarios involving closely spaced targets, the RGDP−SBL algorithm makes use of a three−level hierarchical prior model. Furthermore, the RGDP−SBL algorithm efficiently utilizes diagonal elements during the coarse search and exploits the convexity of the grid energy curve during the fine search, therefore significantly reducing computational complexity. Simulation results demonstrate that the RGDP−SBL algorithm significantly improves signal recovery performance while maintaining low computational complexity in multiple scenarios for RFPA radar. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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23 pages, 10936 KiB

Open AccessArticle

Radio Frequency Interference Mitigation in Synthetic Aperture Radar Data Based on Instantaneous Spectrum Forward Consecutive Mean Excision

by Zijian Wang, Wenbo Yu, Jiamu Li, Zhongjun Yu, Yao Zhao and Yunhua Luo

Remote Sens. 2024, 16(1), 150; https://doi.org/10.3390/rs16010150 - 29 Dec 2023

Viewed by 596

Abstract

Radio frequency interference (RFI) poses major threats to synthetic aperture radar (SAR) systems. Due to the suppression of useful target signals via high-power RFI, the SAR imaging quality is severely degraded. Nevertheless, existing studies on RFI mitigation mainly focus on narrowband filtering, while [...] Read more.

Radio frequency interference (RFI) poses major threats to synthetic aperture radar (SAR) systems. Due to the suppression of useful target signals via high-power RFI, the SAR imaging quality is severely degraded. Nevertheless, existing studies on RFI mitigation mainly focus on narrowband filtering, while wideband RFI mitigation methods are relatively lacking and perform non-robustly. In this paper, an RFI mitigation scheme is proposed based on instantaneous spectrum forward consecutive mean excision (FCME), which is suitable for both narrowband and wideband RFI mitigation. The SAR echo signal is first transformed into a time–frequency (TF) domain through a short-time Fourier transform (STFT). On this basis, the instantaneous spectra polluted via RFI are detected via a kurtosis-based statistical test and then filtered via FCME to achieve RFI mitigation. Finally, connected component analysis is applied as a safety measure so as to avoid the unnecessary loss of useful target signal. The combination of FCME and connected component analysis enables the proposed method to thoroughly filter out RFI while retaining more useful target signals compared with other competing methods. The experimental results on real SAR raw data validate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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25 pages, 29121 KiB

Open AccessArticle

Radar Emitter Signal Intra-Pulse Modulation Open Set Recognition Based on Deep Neural Network

by Shibo Yuan, Peng Li and Bin Wu

Remote Sens. 2024, 16(1), 108; https://doi.org/10.3390/rs16010108 - 26 Dec 2023

Viewed by 687

Abstract

Radar emitter signal intra-pulse modulation recognition is important for modern electronic reconnaissance systems to analyze target radar systems. In the actual environment, the intra-pulse modulations of the sampled radar emitter signals contain not only the known types in the library but also the [...] Read more.

Radar emitter signal intra-pulse modulation recognition is important for modern electronic reconnaissance systems to analyze target radar systems. In the actual environment, the intra-pulse modulations of the sampled radar emitter signals contain not only the known types in the library but also the unknown types. Therefore, the existing recognition methods, which are based on a closed set, cannot recognize the unknown samples. In order to solve this problem, in this paper, we proposed a method for radar emitter signal intra-pulse modulation open set recognition. The proposed method could classify the known modulations and identify the unknown modulation by using an original deep neural network-based recognition model trained on a closed set, estimating the signal-to-noise ratio, and calculating the reconstruction loss by an encoder–decoder model. For a given sample, the original deep neural network-based recognition model will label it as a certain known class temporarily. By estimating the SNR of the sample and calculating the reconstruction loss by inputting the sample to the corresponding encoder–decoder model related to the temporary predicted known class, whether the sample belongs to the predicted temporary known class or the unknown class will be confirmed. Experiments were conducted with five different openness conditions. The experimental results indicate that the proposed method has good performance on radar emitter signal intra-pulse modulation open set recognition. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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15 pages, 6752 KiB

Open AccessCommunication

GPU-Accelerated Signal Processing for Passive Bistatic Radar

by Xinyu Zhao, Peng Liu, Bingnan Wang and Yaqiu Jin

Remote Sens. 2023, 15(22), 5421; https://doi.org/10.3390/rs15225421 - 19 Nov 2023

Viewed by 1184

Abstract

Passive bistatic radar is a novel radar technology that passively detects targets without actively emitting signals. Since passive bistatic radar entails larger data volumes and computations compared to traditional active radiation radar, the development of hardware and software platforms capable of efficiently processing [...] Read more.

Passive bistatic radar is a novel radar technology that passively detects targets without actively emitting signals. Since passive bistatic radar entails larger data volumes and computations compared to traditional active radiation radar, the development of hardware and software platforms capable of efficiently processing signals from passive bistatic radar has emerged as a research focus in this field. This research investigates the signal processing flow of passive bistatic radar based on its characteristics and devises a parallel signal processing scheme under graphic processing unit (GPU) architecture for computation-intensive tasks. The proposed scheme utilizes high-computing-power GPU as the hardware platform and compute unified device architecture (CUDA) as the software platform and optimizes the extensive cancellation algorithm batches (ECA-B), range Doppler and constant false alarm detection algorithms. The detection and tracking of a single target are realized on the passive bistatic radar dataset of natural scenarios, and experiments show that the design of this algorithm can achieve a maximum acceleration ratio of 113.13. Comparative experiments conducted with varying data volumes revealed that this method significantly enhances the signal processing rate for passive bistatic radar. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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17 pages, 2113 KiB

Open AccessArticle

Direction of Arrival Estimation with Nested Arrays in Presence of Impulsive Noise: A Correlation Entropy-Based Infinite Norm Strategy

by Jun Zhao, Renzhou Gui, Xudong Dong, Meng Sun and Yide Wang

Remote Sens. 2023, 15(22), 5345; https://doi.org/10.3390/rs15225345 - 13 Nov 2023

Cited by 1 | Viewed by 797

Abstract

Direction of arrival (DOA) estimation with nested arrays has been widely investigated in the field of array signal processing, but most studies assume that the noise is Gaussian white noise. In practical situations, there may exist impulsive noise (a kind of heavy-tailed noise), [...] Read more.

Direction of arrival (DOA) estimation with nested arrays has been widely investigated in the field of array signal processing, but most studies assume that the noise is Gaussian white noise. In practical situations, there may exist impulsive noise (a kind of heavy-tailed noise), wherein the performance of traditional subspace-based DOA estimation algorithms deteriorates significantly. In this paper, we propose a correlation entropy-based infinite norm preprocessing algorithm, which can be applicable to any type of impulsive noise. Each snapshot of the sensor array data is processed by an exponential kernel function with the infinite norm, which can effectively combat the outliers. Furthermore, we construct the equivalent second-order covariance matrix and perform DOA estimation using classical subspace methods. Simulation results demonstrate the effectiveness of the proposed method for both symmetric

α

-stable distribution and the Gaussian mixture model. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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22 pages, 2600 KiB

Open AccessArticle

Cognitive Radar Waveform Design Method under the Joint Constraints of Transmit Energy and Spectrum Bandwidth

by Chen Yang, Wei Yang, Xiangfeng Qiu, Wenpeng Zhang, Zhejun Lu and Weidong Jiang

Remote Sens. 2023, 15(21), 5187; https://doi.org/10.3390/rs15215187 - 31 Oct 2023

Viewed by 852

Abstract

The water-filling (WF) algorithm is a widely used design strategy in the radar waveform design field to maximize the signal-to-interference-plus-noise ratio (SINR). To address the problem of the poor resolution performance of the waveform caused by the inability to effectively control the bandwidth, [...] Read more.

The water-filling (WF) algorithm is a widely used design strategy in the radar waveform design field to maximize the signal-to-interference-plus-noise ratio (SINR). To address the problem of the poor resolution performance of the waveform caused by the inability to effectively control the bandwidth, a novel waveform-related optimization model is established in this paper. Specifically, a corrected SINR expression is first derived to construct the objective function in our optimization model. Then, equivalent bandwidth and energy constraints are imposed on the waveform to formulate the waveform-related non-convex optimization model. Next, the optimal frequency spectrum is obtained using the Karush–Kuhn–Tucker condition of our non-convex model. Finally, the transmit waveform in the time domain is synthesized under the constant modulus constraint. Different experiments based on simulated and real-measured data are constructed to demonstrate the superior performance of the designed waveform on the SINR and equivalent bandwidth compared to the linear frequency modulated signal and waveform designed by the WF algorithm. In addition, to further evaluate the effectiveness of the proposed algorithm in the application of cognitive radar (CR), a closed-loop radar system design strategy is introduced based on our waveform design method. The experiments under real-measured data confirm the advantages of CR compared to the traditional open-loop radar structure. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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20 pages, 6091 KiB

Open AccessArticle

Multi-Label Radar Compound Jamming Signal Recognition Using Complex-Valued CNN with Jamming Class Representation Fusion

by Yunyun Meng, Lei Yu and Yinsheng Wei

Remote Sens. 2023, 15(21), 5180; https://doi.org/10.3390/rs15215180 - 30 Oct 2023

Cited by 2 | Viewed by 750

Abstract

In the complex battlefield electromagnetic environment, multiple jamming signals can enter the radar receiver simultaneously due to the development of jammers and modulation technology. The received compound jamming signals aggravate the difficulty of recognition and subsequent counter-countermeasure. In the face of strong overlapping [...] Read more.

In the complex battlefield electromagnetic environment, multiple jamming signals can enter the radar receiver simultaneously due to the development of jammers and modulation technology. The received compound jamming signals aggravate the difficulty of recognition and subsequent counter-countermeasure. In the face of strong overlapping signals and unseen jamming signal combinations, the performance of existing recognition methods usually seriously degrades. In this paper, an end-to-end multi-label classification framework combining a complex-valued convolutional neural network (CV-CNN) and jamming class representations is proposed to automatically recognize the jamming signal components of compound jamming signals. A basic multi-label CV-CNN (ML-CV-CNN) is first designed to directly process time–domain complex signals and fully retain jamming signal information. Then, the jamming class representations are generated using prototype clustering implemented by learning vector quantization, and they are fused with the ML-CV-CNN using class decoupling implemented by the attention mechanism to construct a multi-label class representation CV-CNN (ML-CR-CV-CNN), which can better learn the class-related features required for recognition. Finally, an adaptive threshold calibration is adopted to obtain optimal recognition results by multi-threshold discrimination. Simulation results verify that the proposed method has superior recognition performance, which is reflected in the strong robustness to the varying jamming-to-noise ratio (JNR) and power ratio, faster convergence speed with high JNRs, and better generalization for unseen jamming signal combinations. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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25 pages, 9019 KiB

Open AccessArticle

Radar Emitter Structure Inversion Method Based on Metric and Deep Learning

by Lutao Liu, Wei Zhang, Yilin Jiang, Yaozu Yang and Yu Song

Remote Sens. 2023, 15(19), 4844; https://doi.org/10.3390/rs15194844 - 06 Oct 2023

Viewed by 751

Abstract

With the rapid development of modern military countermeasure technology, deep distinguish hostile radar is essential in electronic warfare. However, traditional radio frequency (RF) feature extraction methods can easily be interfered by signal information and fail due to the lack of research on RF [...] Read more.

With the rapid development of modern military countermeasure technology, deep distinguish hostile radar is essential in electronic warfare. However, traditional radio frequency (RF) feature extraction methods can easily be interfered by signal information and fail due to the lack of research on RF feature extraction techniques for complex situations. Therefore, in this paper, first, the generation mechanism of RF structure information is discussed, and the influence of different signal information introduced by different operating parameters on RF structure feature extraction is analyzed. Then, an autoencoder (AE) network and an autoencoder metric (AEM) network are designed, introducing metric learning ideas, so that the extracted deep RF structure features have good stability and divisibility. Finally, radar emitter structure (RES) inversion is realized using the centroid-matching method. The experimental results demonstrate that this method exhibits good inversion performance under variable operating parameters (modulation type, frequency, bandwidth, input power). RES inversion including unknown operating parameters is realized for the first time, and it is shown that metric learning has the advantage of separability of RF feature extraction, which can provide an idea in emitter and RF feature extraction. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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28 pages, 7400 KiB

Open AccessArticle

Decentralized Approach for Translational Motion Estimation with Multistatic Inverse Synthetic Aperture Radar Systems

by Alejandro Testa, Debora Pastina and Fabrizio Santi

Remote Sens. 2023, 15(18), 4372; https://doi.org/10.3390/rs15184372 - 05 Sep 2023

Viewed by 630

Abstract

This paper addresses the estimation of the target translational motion by using a multistatic Inverse Synthetic Aperture Radar (ISAR) system composed of an active radar sensor and multiple receiving-only devices. Particularly, a two-step decentralized technique is derived: the first step estimates specific signal [...] Read more.

This paper addresses the estimation of the target translational motion by using a multistatic Inverse Synthetic Aperture Radar (ISAR) system composed of an active radar sensor and multiple receiving-only devices. Particularly, a two-step decentralized technique is derived: the first step estimates specific signal parameters (i.e., Doppler frequency and Doppler rate) at the single-sensor level, while the second step exploits these estimated parameters to derive the target velocity and acceleration components. Specifically, the second step is organized in two stages: the former is for velocity estimation, while the latter is devoted to velocity estimation refinement if a constant velocity model motion can be regarded as acceptable, or to acceleration estimation if a constant velocity assumption does not apply. A proper decision criterion to select between the two motion models is also provided. A closed-form theoretical performance analysis is provided for the overall technique, which is then used to assess the achievable performance under different distributions of the radar sensors. Additionally, a comparison with a state-of-the-art centralized approach has been carried out considering computational burden and robustness. Finally, results obtained against experimental multisensory data are shown confirming the effectiveness of the proposed technique and supporting its practical application. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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19 pages, 20906 KiB

Open AccessArticle

A Modified Range Doppler Algorithm for High-Squint SAR Data Imaging

by Yanan Guo, Pengbo Wang, Zhirong Men, Jie Chen, Xinkai Zhou, Tao He and Lei Cui

Remote Sens. 2023, 15(17), 4200; https://doi.org/10.3390/rs15174200 - 26 Aug 2023

Cited by 2 | Viewed by 1114

Abstract

The high-squint airborne Synthetic Aperture Radar (SAR) has the ability to detect the target area flexibly, and the detection swath is significantly increased compared with the side-looking SAR system. Therefore, it is of great significance to carry out research on high-precision imaging methods [...] Read more.

The high-squint airborne Synthetic Aperture Radar (SAR) has the ability to detect the target area flexibly, and the detection swath is significantly increased compared with the side-looking SAR system. Therefore, it is of great significance to carry out research on high-precision imaging methods for high-squint airborne SAR. However, the high-squint SAR echoes have large Range Cell Migration (RCM), resulting in severe range–azimuth coupling and strong spatial variation. In this paper, a Modified Range Doppler Algorithm (MRDA) is proposed to cope with these effects introduced by the significant RCM in high-squint airborne SAR imaging. The bulk compensation preprocessing is first adopted to remove the considerable RCM and severe cross-coupling in a two-dimensional frequency domain. Then, Non-Linear Chirp Scaling (NLCS) in the range direction is utilized to equalize the range-variant chirp rate caused by the residual RCM and coupling and, therefore, the consistent range phase compensation can be fulfilled in range frequency domain. The modified correlation processing is executed to compensate the residual Doppler phase modulation, the residual RCM and the range-variant cubic phase modulation, which guarantees the characteristics of high efficiency and high precision. The simulations have demonstrated that the MRDA can focus the SAR echoes with large squint angles more effectively than the algorithms based on the Linear Range Walk Correction (LRWC) method. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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16 pages, 3229 KiB

Open AccessArticle

An Optimization Method for Radar Anti-Jamming Strategy via Key Time Nodes

by Cheng Feng, Xiongjun Fu, Jian Dong, Zhichun Zhao, Jiyang Yu and Teng Pan

Remote Sens. 2023, 15(15), 3716; https://doi.org/10.3390/rs15153716 - 25 Jul 2023

Viewed by 761

Abstract

This paper proposes an optimization method to improve the radar anti-jamming strategy by using the predictability of left game interval. Firstly, we propose the concept of key time nodes in radar/jammer confrontation and analyze its predictability. Secondly, we analyze the radar-winning scenarios by [...] Read more.

This paper proposes an optimization method to improve the radar anti-jamming strategy by using the predictability of left game interval. Firstly, we propose the concept of key time nodes in radar/jammer confrontation and analyze its predictability. Secondly, we analyze the radar-winning scenarios by considering the temporal constraints and construct the actual utility matrix of the radar. Then, we describe the optimization algorithm using radar-winning probability statistics based on the prediction of left game interval. Finally, we carry out a simulation experiment by comparing it with other anti-jamming strategies to verify the rationality, and the result shows that the proposed method can significantly improve the radar’s winning probability in the confrontation. By using the proposed anti-jamming strategy optimization method just at the key time nodes, the imperceptibility from the jammer is improved, and its long-term superiority can be maintained in the confrontation. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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17 pages, 26690 KiB

Open AccessArticle

DRFM Repeater Jamming Suppression Method Based on Joint Range-Angle Sparse Recovery and Beamforming for Distributed Array Radar

by Bowen Han, Xiaodong Qu, Xiaopeng Yang, Zhengyan Zhang and Wolin Li

Remote Sens. 2023, 15(13), 3449; https://doi.org/10.3390/rs15133449 - 07 Jul 2023

Cited by 1 | Viewed by 980

Abstract

Distributed array radar achieves high angular resolution and measurement accuracy, which could provide a solution to suppress digital radio frequency memory (DRFM) repeater jamming. However, owing to the large aperture of a distributed radar, the far-field plane wave assumption is no longer satisfied. [...] Read more.

Distributed array radar achieves high angular resolution and measurement accuracy, which could provide a solution to suppress digital radio frequency memory (DRFM) repeater jamming. However, owing to the large aperture of a distributed radar, the far-field plane wave assumption is no longer satisfied. Consequently, traditional adaptive beamforming methods cannot work effectively due to mismatched steering vectors. To address this issue, a DRFM repeater jamming suppression method based on joint range-angle sparse recovery and beamforming for distributed array radar is proposed in this paper. First, the steering vectors of the distributed array are reconstructed according to the spherical wave model under near-field conditions. Then, a joint range-angle sparse dictionary is generated using reconstructed steering vectors, and the range-angle position of jamming is estimated using the weighted L1-norm singular value decomposition (W-L1-SVD) algorithm. Finally, beamforming with joint range-angle nulling is implemented based on the linear constrained minimum variance (LCMV) algorithm for jamming suppression. The performance and effectiveness of proposed method is validated by simulations and experiments on an actual ground-based distributed array radar system. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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21 pages, 11276 KiB

Open AccessArticle

Gaussian Process Gaussian Mixture PHD Filter for 3D Multiple Extended Target Tracking

by Zhiyuan Yang, Xiangqian Li, Xianxun Yao, Jinping Sun and Tao Shan

Remote Sens. 2023, 15(13), 3224; https://doi.org/10.3390/rs15133224 - 21 Jun 2023

Cited by 4 | Viewed by 1106

Abstract

This paper addresses the problem of tracking multiple extended targets in three-dimensional space. We propose the Gaussian process Gaussian mixture probability hypothesis density (GP-PHD) filter, which is capable of tracking multiple extended targets with complex shapes in the presence of clutter. Our approach [...] Read more.

This paper addresses the problem of tracking multiple extended targets in three-dimensional space. We propose the Gaussian process Gaussian mixture probability hypothesis density (GP-PHD) filter, which is capable of tracking multiple extended targets with complex shapes in the presence of clutter. Our approach combines the Gaussian process regression measurement model with the probability hypothesis density filter to estimate both the kinematic state and the shape of the targets. The shape of the extended target is described by a 3D radial function and is estimated recursively using the Gaussian process regression model. Furthermore, we transform the recursive Gaussian process regression problem into a state estimation problem by deriving a state space model such that the estimation of the extent can be integrated into the kinematic part. We derive the predicted likelihood function of the PHD filter and provide a closed-form Gaussian mixture implementation. To evaluate the performance of the proposed filter, we simulate a typical extended target tracking scenario and compare the GP-PHD filter with the traditional Gamma Gaussian Inverse-Wishart PHD (GGIW-PHD) filter. Our results demonstrate that the proposed algorithm outperforms the GGIW-PHD filter in terms of estimating both kinematic states and shape. We also investigate the impact of the measurement rates on both filters; it is observed that the proposed filter exhibits robustness across various measurement rates, while the GGIW-PHD filter suffers under low-measurement-rate conditions. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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25 pages, 1756 KiB

Open AccessArticle

An Anti-Intermittent Sampling Jamming Technique Utilizing the OTSU Algorithm of Random Orthogonal Sub-Pulses

by Haihong Zhan, Tao Wang, Tai Guo and Xingde Su

Remote Sens. 2023, 15(12), 3080; https://doi.org/10.3390/rs15123080 - 12 Jun 2023

Cited by 1 | Viewed by 1065

Abstract

The utilization of intermittent sampling jamming can engender a lofty verisimilitude false target cluster that exhibits coherence with the transmitted signal. Such an assemblage bears the hallmarks of both suppression jamming and deceitful jamming, capable of inflicting substantial impairment upon the radar, potentially [...] Read more.

The utilization of intermittent sampling jamming can engender a lofty verisimilitude false target cluster that exhibits coherence with the transmitted signal. Such an assemblage bears the hallmarks of both suppression jamming and deceitful jamming, capable of inflicting substantial impairment upon the radar, potentially leading to its profound incapacitation. Henceforth, the precise discernment of the target and various forms of intermittent sampling jamming emerges as a novel endeavor. In response to this predicament, this paper posits a pulsed radar waveform featuring intra-pulse random orthogonal frequency modulation (FM) and inter-pulse phase coherence. This innovative approach not only presents formidable challenges for the jammer in acquiring radar waveform parameters, but also bolsters the radar’s low probability of intercept (LPI), while maintaining the phase coherence of sub-pulses between pulses. Furthermore, based on this waveform, the characteristics of the intermittent sampling jamming signal and its differences from the target echo signal are analyzed in the time domain, frequency domain, time-frequency domain, and pulse compression domain. Building upon these findings, this paper proposes the sub-division algorithms for typical types of intermittent sampling jamming under this waveform: the full-pulses multi-level maximum inter-class variance and sub-pulses multi-level maximum inter-class variance anti-intermittent sampling jamming algorithms. Simulation analysis demonstrates that this waveform and the anti-jamming algorithms can accurately identify and effectively counteract different types of intermittent sampling jamming in typical scenarios. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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17 pages, 2891 KiB

Open AccessArticle

An Optimization Method for Collaborative Radar Antijamming Based on Multi-Agent Reinforcement Learning

by Cheng Feng, Xiongjun Fu, Ziyi Wang, Jian Dong, Zhichun Zhao and Teng Pan

Remote Sens. 2023, 15(11), 2893; https://doi.org/10.3390/rs15112893 - 01 Jun 2023

Cited by 1 | Viewed by 1444

Abstract

Attacking a naval vessel with multiple missiles is an important way to improve the hit rate of missiles. Missile-borne radars need to complete detection and antijamming tasks to guide missiles, but communication between these radars is often difficult. In this paper, an optimization [...] Read more.

Attacking a naval vessel with multiple missiles is an important way to improve the hit rate of missiles. Missile-borne radars need to complete detection and antijamming tasks to guide missiles, but communication between these radars is often difficult. In this paper, an optimization method based on multi-agent reinforcement learning is proposed for the collaborative detection and antijamming tasks of multiple radars against one naval vessel. We consider the collaborative radars as one player to make their confrontation with the naval vessel a two-person zero-sum game. With temporal constraints of the radar’s and jammer’s recognition and preparation interval, the game focuses on taking a favorable position at the end of the confrontation. It is assumed the total jamming capability of a shipborne jammer is constant and limited, and the shipborne jammer allocates the jamming capability in the radar’s direction according to the radar threat assessment result and its probability of successful detection. The radars work collaboratively through prior centralized training and obtain a good performance by decentralized execution. The proposed method can make radars collaborate to detect the naval vessel, rather than only considering the detection result of each radar itself. Experimental results show that the proposed method in this paper is effective, improving the winning probability to 10% and 25% in the two-radar and four-radar scenarios, respectively. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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22 pages, 13885 KiB

Open AccessArticle

Radar Maneuvering Target Detection Based on Product Scale Zoom Discrete Chirp Fourier Transform

by Lang Xia, Huotao Gao, Lizheng Liang, Taoming Lu and Boning Feng

Remote Sens. 2023, 15(7), 1792; https://doi.org/10.3390/rs15071792 - 27 Mar 2023

Cited by 2 | Viewed by 1073

Abstract

Long-time coherent integration works to significantly increase the detection probability for maneuvering targets. However, during the observation time, the problems that often tend to occur are range cell migration (RCM) and Doppler frequency cell migration (DFCM), due to the high velocity and acceleration [...] Read more.

Long-time coherent integration works to significantly increase the detection probability for maneuvering targets. However, during the observation time, the problems that often tend to occur are range cell migration (RCM) and Doppler frequency cell migration (DFCM), due to the high velocity and acceleration of the maneuvering target, which can reduce the detection of the maneuvering targets. In this regard, we propose a new coherent integration approach, based on the product scale zoom discrete chirp Fourier transform (PSZDCFT). Specifically, by introducing the zoom operation into the modified discrete chirp Fourier transform (MDCFT), the zoom discrete chirp Fourier transform (ZDCFT) can correctly estimate the centroid frequency and chirp rate of the linear frequency-modulated signal (LFM), regardless of whether the parameters of the LFM signal are outside the estimation scopes. Then, the scale operation, combined with ZDCFT, is performed on the radar echo signal in the range frequency slow time domain, to remove the coupling. Thereafter, a product operation is executed along the range frequency to inhibit spurious peaks and reinforce the true peak. Finally, the velocity and acceleration of the target estimated from the true peak position, are used to construct a phase compensation function to eliminate the RCM and DFCM, thus achieving coherent integration. The method is a linear transform without energy loss, and is suitable for low signal-to-noise (SNR) environments. Moreover, the method can be effectively fulfilled based on the chirp-z transform (CZT), which prevents the brute-force search. Thus, the method reaches a favorable tradeoff between anti-noise performance and computational load. Intensive simulations demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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16 pages, 7757 KiB

Open AccessArticle

DRFM-Based Repeater Jamming Reconstruction and Cancellation Method with Accurate Edge Detection

by Bowen Han, Xiaodong Qu, Xiaopeng Yang, Wolin Li and Zhengyan Zhang

Remote Sens. 2023, 15(7), 1759; https://doi.org/10.3390/rs15071759 - 24 Mar 2023

Cited by 3 | Viewed by 1484

Abstract

Digital radio frequency memory (DRFM) based repeater jamming can create false targets, which can lead to a loss of situational awareness, misidentification of targets, and decreased overall performance of the radar system. Traditional jamming suppression methods do not give due importance to the [...] Read more.

Digital radio frequency memory (DRFM) based repeater jamming can create false targets, which can lead to a loss of situational awareness, misidentification of targets, and decreased overall performance of the radar system. Traditional jamming suppression methods do not give due importance to the accurate estimation of the jamming edge, resulting in jamming residual and poor anti-jamming performance. To tackle this issue, this paper explores the reason and impact of inaccurate jamming edge estimation and proposes a DRFM-based repeater jamming reconstruction and cancellation method with accurate edge detection. In the proposed method, firstly, multiple jamming parameters are obtained by computing the short-time fractional Fourier transformation (STFRFT) spectrogram of the received signal. To avoid jamming residue, the proposed method estimates the accurate jamming edges by the joint use of the difference of box (DOB) filters and time domain deconvolution (TDDC) curves. Numerical simulations and experiments are conducted to evaluate the algorithm’s effectiveness in countering smeared spectrum (SMSP) and interrupted sampling repeater jamming (ISRJ). The results demonstrate its superior jamming reconstruction and suppression performance than other methods. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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12 pages, 3868 KiB

Open AccessTechnical Note

Chaotic Coding for Interference Suppression of Digital Ionosonde

by Sijia Han, Wei Guo, Peng Liu, Te Wang, Caiyun Wang, Qingyu Fang, Jian Yang, Lingling Li, Dapeng Liu and Jianping Huang

Remote Sens. 2023, 15(15), 3747; https://doi.org/10.3390/rs15153747 - 27 Jul 2023

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Abstract

External interference in ionospheric sounding seriously degrades the quality of echo signals and data; thus, it should be eliminated. This paper presents a method for suppressing interference using chaotic coding with a set of Bernoulli map sequences; compared with other commonly used coding [...] Read more.

External interference in ionospheric sounding seriously degrades the quality of echo signals and data; thus, it should be eliminated. This paper presents a method for suppressing interference using chaotic coding with a set of Bernoulli map sequences; compared with other commonly used coding methods such as Barker code, complementary code, and Barker-like codes, through simulation, the ambiguity function (AF) of Bernoulli map codes has better performance in terms of peak sidelobe level (PSL), integral sidelobe ratio (ISL), noise suppression (NS), and signal-to-noise ratio (SNR). Experimental tests were performed using a vertical ionosonde in Yinchuan, Ningxia Hui Autonomous Region, China, and the ionosonde was operated by alternating 40-bit Barker-like coding and 40-bit Bernoulli map coding each day to compare the effectiveness of interference suppression. The results showed that using Bernoulli map coding could remove interference and improve SNR significantly, thereby improving the data quality of the resulting ionograms. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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16 pages, 4017 KiB

Open AccessTechnical Note

Modeling and Dynamic Radar Cross-Section Estimation of Chaff Clouds for Real-Time Simulation

by Jun-Seon Kim, Uk Jin Jung, Su-Hong Park, Dong-Yeob Lee, Moonhong Kim, Dongwoo Sohn and Dong-Wook Seo

Remote Sens. 2023, 15(14), 3587; https://doi.org/10.3390/rs15143587 - 18 Jul 2023

Viewed by 1526

Abstract

Chaff is a passive jammer widely used to disrupt radar or radio-frequency sensors. A mass of chaff fibers dispersed in the air is commonly referred to as a chaff cloud. It is nearly impossible to numerically simulate in real-time the enormous amount of [...] Read more.

Chaff is a passive jammer widely used to disrupt radar or radio-frequency sensors. A mass of chaff fibers dispersed in the air is commonly referred to as a chaff cloud. It is nearly impossible to numerically simulate in real-time the enormous amount of chaff fibers composing the chaff cloud. In this paper, we model the behavior of numerically estimated chaff clouds as probability density functions (PDFs) and apply approximation techniques to estimate the radar cross-section (RCS) of the chaff cloud in real time. To model the aerodynamics of the chaff cloud, we represented the combination of PDFs as functions of time and wind speed. The applied approximation techniques—vector radiative transfer and generalized equivalent conductor method—showed a computation time that cannot be achieved by low-frequency methods such as the method of moments or finite-difference time-domain. Moreover, the dynamic RCS results of the approximation techniques showed a similar trend to those of other studies simulating similar situations. The proposed scheme is effective for real-time chaff cloud simulation, and the modeled dynamics and estimated dynamic RCSs can be a standard baseline for developing new analysis methods for chaff clouds. In the future, the proposed scheme will extend to more chaff fibers and more diverse environmental parameters. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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22 pages, 9638 KiB

Open AccessTechnical Note

Periodic-Filtering Method for Low-SNR Vibration Radar Signal

by Yun Lin, Linghan Zhang, Hongwei Han, Yang Li, Wenjie Shen and Yanping Wang

Remote Sens. 2023, 15(14), 3461; https://doi.org/10.3390/rs15143461 - 08 Jul 2023

Cited by 1 | Viewed by 999

Abstract

Radar is a non-contact, high-precision vibration measurement device and an important tool for bridge vibration monitoring. Vibration information needs to be extracted from the radar phase, but the radar phase information is sensitive to noise. Under low signal-to-noise ratio (SNR) data acquisition conditions, [...] Read more.

Radar is a non-contact, high-precision vibration measurement device and an important tool for bridge vibration monitoring. Vibration information needs to be extracted from the radar phase, but the radar phase information is sensitive to noise. Under low signal-to-noise ratio (SNR) data acquisition conditions, such as low radar transmission power or a long observation distance, differential phase jump errors occur and clutter estimation becomes difficult, which leads to inaccurate inversion of vibration deformation. Traditional low-pass filtering methods can filter out noise to improve SNR, but they require oversampling. The sampling rate needs to be several times higher than the Doppler bandwidth, which is several times higher than the vibration frequency. This puts high data acquisition requirements on radar systems and causes large data volumes. Therefore, this paper proposes a novel vibration signal filtering method called the periodic filtering method. The method uses the periodicity feature of vibration signals for filtering without oversampling. This paper derives the time-domain and frequency-domain expressions for the periodic filter and presents a deformation inversion process based on them. The process involves extracting the vibration frequency in the Doppler domain, suppressing noise through periodic filtering, estimating clutter using circle fitting on the data complex plane, and inverting final deformation with differential phase. The method is verified through simulation experiments, calibration experiments, and bridge vibration experiments. The results show that the new periodic filtering method can improve the SNR by five times, resolve differential phase jumps, and accurately estimate clutter, thus getting submillimeter-level vibration deformation at low SNR. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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14 pages, 2911 KiB

Open AccessTechnical Note

Fast Frequency-Diverse Radar Imaging Based on Adaptive Sampling Iterative Soft-Thresholding Deep Unfolding Network

by Zhenhua Wu, Fafa Zhao, Lei Zhang, Yice Cao, Jun Qian, Jiafei Xu and Lixia Yang

Remote Sens. 2023, 15(13), 3284; https://doi.org/10.3390/rs15133284 - 26 Jun 2023

Viewed by 899

Abstract

Frequency-diverse radar imaging is an emerging field that combines computational imaging with frequency-diverse techniques to interrogate the high-quality images of objects. Despite the success of deep reconstruction networks in improving scene image reconstruction from noisy or under-sampled frequency-diverse measurements, their reliance on large [...] Read more.

Frequency-diverse radar imaging is an emerging field that combines computational imaging with frequency-diverse techniques to interrogate the high-quality images of objects. Despite the success of deep reconstruction networks in improving scene image reconstruction from noisy or under-sampled frequency-diverse measurements, their reliance on large amounts of high-quality training data and the inherent uninterpretable features pose significant challenges in the design and optimization of imaging networks, particularly in the face of dynamic variations in radar operating frequency bands. Here, aiming at reducing the latency and processing burden involved in scene image reconstruction, we propose an adaptive sampling iterative soft-thresholding deep unfolding network (ASISTA-Net). Specifically, we embed an adaptively sampling module into the iterative soft-thresholding (ISTA) unfolding network, which contains multiple measurement matrices with different compressed sampling ratios. The outputs of the convolutional layers are then passed through a series of ISTA layers that perform a sparse coding step followed by a thresholding step. The proposed method requires no need for heavy matrix operations and massive amount of training scene targets and measurements datasets. Unlike recent work using matrix-inversion-based and data-driven deep reconstruction networks, our generic approach is directly adapted to multi-compressed sampling ratios and multi-scene target image reconstruction, and no restrictions on the types of imageable scenes are imposed. Multiple measurement matrices with different scene compressed sampling ratios are trained in parallel, which enables the frequency-diverse radar to select operation frequency bands flexibly. In general, the application of the proposed approach paves the way for the widespread deployment of computational microwave and millimeter wave frequency-diverse radar imagers to achieve real-time imaging. Extensive imaging simulations demonstrate the effectiveness of our proposed method. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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