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20 pages, 6644 KiB

Open AccessArticle

A Novel ISAR Image Feature Suppression Method Based on Arbitrary Phase Encoding

by Yanfeng Wang, Qihua Wu, Xiaobin Liu, Zhiming Xu, Feng Zhao and Shunping Xiao

Remote Sens. 2024, 16(21), 3960; https://doi.org/10.3390/rs16213960 - 24 Oct 2024

Viewed by 432

Abstract

Compared with the amplitude modulation of conventional interrupted sampling repeater jamming (ISRJ), the image feature control method based on phase modulation exhibits greater energy efficiency and, therefore, has received wide attention recently. In this paper, an Inverse Synthetic Aperture Radar (ISAR) image feature [...] Read more.

Compared with the amplitude modulation of conventional interrupted sampling repeater jamming (ISRJ), the image feature control method based on phase modulation exhibits greater energy efficiency and, therefore, has received wide attention recently. In this paper, an Inverse Synthetic Aperture Radar (ISAR) image feature suppression method based on arbitrary phase encoding (APE) is proposed. The parameter design criterion is further analyzed. Through the nonperiodic segmented coding and modulation of the imaging signal in fast and slow time domains, the modulated signal produces a two-dimensional suppression region with uniform energy distribution in the ISAR image. Simulations via the measured Yak-42 aircraft data have verified the effectiveness of the proposed method for target feature control. Compared to binary phase modulation jamming, the APE method with a phase modulation accuracy of 1 degree can achieve the same jamming effect while reducing the jamming power requirement by 3 dB. By optimizing with the proposed method, the image entropy of the interfered image increases by 1.1 to 1.5 compared to the original image. Full article

(This article belongs to the Special Issue State-of-the-Art and Future Developments: Short-Range Radar)

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17 pages, 10820 KiB

Open AccessArticle

Multiple-Input Multiple-Output Microwave Tomographic Imaging for Distributed Photonic Radar Network

by Carlo Noviello, Salvatore Maresca, Gianluca Gennarelli, Antonio Malacarne, Filippo Scotti, Paolo Ghelfi, Francesco Soldovieri, Ilaria Catapano and Rosa Scapaticci

Remote Sens. 2024, 16(21), 3940; https://doi.org/10.3390/rs16213940 - 23 Oct 2024

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Abstract

This paper deals with the imaging problem from data collected by means of a microwave photonics-based distributed radar network. The radar network is leveraged on a centralized architecture, which is composed of one central unit (CU) and two transmitting and receiving dual-band remote [...] Read more.

This paper deals with the imaging problem from data collected by means of a microwave photonics-based distributed radar network. The radar network is leveraged on a centralized architecture, which is composed of one central unit (CU) and two transmitting and receiving dual-band remote radar peripherals (RPs), it is capable of collecting monostatic and multistatic phase-coherent data. The imaging is herein formulated as a linear inverse scattering problem and solved in a regularized way through the truncated singular value decomposition inversion scheme. Specifically, two different imaging schemes based on an incoherent fusion of the tomographic images or a fully coherent data processing are herein developed and compared. Experimental tests carried out in a port scenario for imaging both a stationary and a moving target are reported to validate the imaging approach. Full article

(This article belongs to the Special Issue State-of-the-Art and Future Developments: Short-Range Radar)

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25 pages, 12863 KiB

Open AccessArticle

Design of a Near-Field Synthetic Aperture Radar Imaging System Based on Improved RMA

by Yongcheng Li, Huaqiang Xu, Jiawei Xu, Hao Chen, Qiying An, Kangming Hou and Jingjing Wang

Remote Sens. 2024, 16(17), 3342; https://doi.org/10.3390/rs16173342 - 9 Sep 2024

Viewed by 970

Abstract

Traditional near-field synthetic aperture radar (SAR) imaging algorithms reveal target features by exploiting signal amplitude and phase information. However, electromagnetic wave propagation is constrained by short distance. Therefore, the spherical wave approximation needs to be considered. In addition, it is also limited by [...] Read more.

Traditional near-field synthetic aperture radar (SAR) imaging algorithms reveal target features by exploiting signal amplitude and phase information. However, electromagnetic wave propagation is constrained by short distance. Therefore, the spherical wave approximation needs to be considered. In addition, it is also limited by equipment ambient noise, azimuth-distance coupling, wave scattering, and transmission power. Both the amplitude and phase of the signal suffer from the interference of multiple clutter, so they cannot be effectively utilized. To address these issues, this paper introduces a covering penetration detection system based on an improved Range Migration Algorithm (IMRMA) imaging method. Firstly, the proposed method minimizes interferences from the front end of the system using an optimized window to balance denoising and information preservation. Next, interval non-uniform interpolation, instead of Stolt interpolation decoupling, is employed to reduce the computational overhead significantly. To minimize the effects due to wave scattering and propagation loss, distance information is enhanced using amplitude and phase compensation. This reduces scattering effects and enhances image quality. An experimental system is constructed based on a vector network analyzer (VNA) to image the target. The proposed method takes about half the time of traditional RMA. The PSNR in the chunky bowl experiment is higher than 14 dB, which is higher than all the compared methods in the paper. The test results show that the designed system and the reported method can effectively achieve high-resolution images by strengthening the target intensity and suppressing the environmental artifacts. Full article

(This article belongs to the Special Issue State-of-the-Art and Future Developments: Short-Range Radar)

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24 pages, 13925 KiB

Open AccessArticle

Millimeter-Wave Radar Detection and Localization of a Human in Indoor Complex Environments

by Zhixuan Xing, Penghui Chen, Jun Wang, Yujing Bai, Jinhao Song and Liuyang Tian

Remote Sens. 2024, 16(14), 2572; https://doi.org/10.3390/rs16142572 - 13 Jul 2024

Viewed by 1022

Abstract

Nowadays, it is still a great challenge to detect and locate indoor humans using a frequency-modulated continuous-wave radar accurately. Due to the interference of the indoor environment and complex objects such as green plants, the radar signal may penetrate, reflect, refract, and scatter, [...] Read more.

Nowadays, it is still a great challenge to detect and locate indoor humans using a frequency-modulated continuous-wave radar accurately. Due to the interference of the indoor environment and complex objects such as green plants, the radar signal may penetrate, reflect, refract, and scatter, and the echo signals will contain noise, clutter, and multipath of different characteristics. Therefore, a method combined with comprehensive non-target signal removal and human localization is proposed to achieve position estimation of a human target. Time-variant clutter is innovatively mitigated through time accumulation using point clustering. Ghost targets are reduced according to propagation path matching. The experimental results show that the method can locate the real target human within an average error of 0.195 m in multiple complex environments with green plants, curtains, or furniture using a 77 GHz millimeter-wave radar. Meanwhile, the proposed method performs better than conventional methods. The detection probability is 81.250% when the human is behind a potted plant and is 90.286% when beside it. Full article

(This article belongs to the Special Issue State-of-the-Art and Future Developments: Short-Range Radar)

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32 pages, 8146 KiB

Open AccessArticle

SCRP-Radar: Space-Aware Coordinate Representation for Human Pose Estimation Based on SISO UWB Radar

by Xiaolong Zhou, Tian Jin, Yongpeng Dai, Yongping Song and Kemeng Li

Remote Sens. 2024, 16(9), 1572; https://doi.org/10.3390/rs16091572 - 28 Apr 2024

Cited by 1 | Viewed by 1288

Abstract

Human pose estimation (HPE) is an integral component of numerous applications ranging from healthcare monitoring to human-computer interaction, traditionally relying on vision-based systems. These systems, however, face challenges such as privacy concerns and dependency on lighting conditions. As an alternative, short-range radar technology [...] Read more.

Human pose estimation (HPE) is an integral component of numerous applications ranging from healthcare monitoring to human-computer interaction, traditionally relying on vision-based systems. These systems, however, face challenges such as privacy concerns and dependency on lighting conditions. As an alternative, short-range radar technology offers a non-invasive, lighting-insensitive solution that preserves user privacy. This paper presents a novel radar-based framework for HPE, SCRP-Radar (space-aware coordinate representation for human pose estimation using single-input single-output (SISO) ultra-wideband (UWB) radar). The methodology begins with clutter suppression and denoising techniques to enhance the quality of radar echo signals, followed by the construction of a micro-Doppler (MD) matrix from these refined signals. This matrix is segmented into bins to extract distinctive features that are critical for pose estimation. The SCRP-Radar leverages the Hrnet and LiteHrnet networks, incorporating space-aware coordinate representation to reconstruct 2D human poses with high precision. Our method redefines HPE as dual classification tasks for vertical and horizontal coordinates, which is a significant departure from existing methods such as RF-Pose, RF-Pose 3D, UWB-Pose, and RadarFormer. Extensive experimental evaluations demonstrate that SCRP-Radar significantly surpasses these methods in accuracy and robustness, consistently exhibiting lower average error rates, achieving less than 40 mm across 17 skeletal key-points. This innovative approach not only enhances the precision of radar-based HPE but also sets a new benchmark for future research and application, particularly in sectors that benefit from accurate and privacy-preserving monitoring technologies. Full article

(This article belongs to the Special Issue State-of-the-Art and Future Developments: Short-Range Radar)

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21 pages, 3407 KiB

Open AccessArticle

Radar Signal Classification with Multi-Frequency Multi-Scale Deformable Convolutional Networks and Attention Mechanisms

by Ruofei Liang and Yigang Cen

Remote Sens. 2024, 16(8), 1431; https://doi.org/10.3390/rs16081431 - 18 Apr 2024

Cited by 1 | Viewed by 1243

Abstract

In the realm of short-range radar applications, the focus on detecting “low, slow, and small” (LSS) targets has escalated, marking a pivotal aspect of critical area defense. This study pioneers the use of one-dimensional convolutional neural networks (1D-CNNs) for direct slow-time dimension radar [...] Read more.

In the realm of short-range radar applications, the focus on detecting “low, slow, and small” (LSS) targets has escalated, marking a pivotal aspect of critical area defense. This study pioneers the use of one-dimensional convolutional neural networks (1D-CNNs) for direct slow-time dimension radar feature extraction, sidestepping the complexity tied to frequency and wavelet domain transformations. It innovatively employs a network architecture enriched with multi-frequency multi-scale deformable convolution (MFMSDC) layers for nuanced feature extraction, integrates attention modules to foster comprehensive feature connectivity, and leverages linear operations to curtail overfitting. Through comparative evaluations and ablation studies, our methodology not only simplifies the analytic process but also demonstrates superior classification capabilities. This establishes a new benchmark for efficiently classifying low-altitude entities, such as birds and unmanned aerial vehicles (UAVs), thereby enhancing the precision and operational efficiency of radar detection systems. Full article

(This article belongs to the Special Issue State-of-the-Art and Future Developments: Short-Range Radar)

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23 pages, 21681 KiB

Open AccessArticle

LDnADMM-Net: A Denoising Unfolded Deep Neural Network for Direction-of-Arrival Estimations in A Low Signal-to-Noise Ratio

by Can Liang, Mingxuan Liu, Yang Li, Yanhua Wang and Xueyao Hu

Remote Sens. 2024, 16(3), 554; https://doi.org/10.3390/rs16030554 - 31 Jan 2024

Cited by 2 | Viewed by 1294

Abstract

In this paper, we explore the problem of direction-of-arrival (DOA) estimation for a non-uniform linear array (NULA) under strong noise. The compressed sensing (CS)-based methods are widely used in NULA DOA estimations. However, these methods commonly rely on the tuning of parameters, which [...] Read more.

In this paper, we explore the problem of direction-of-arrival (DOA) estimation for a non-uniform linear array (NULA) under strong noise. The compressed sensing (CS)-based methods are widely used in NULA DOA estimations. However, these methods commonly rely on the tuning of parameters, which are hard to fine-tune. Additionally, these methods lack robustness under strong noise. To address these issues, this paper proposes a novel DOA estimation approach using a deep neural network (DNN) for a NULA in a low SNR. The proposed network is designed based on the denoising convolutional neural network (DnCNN) and the alternating direction method of multipliers (ADMM), which is dubbed as LDnADMM-Net. First, we construct an unfolded DNN architecture that mimics the behavior of the iterative processing of an ADMM. In this way, the parameters of an ADMM can be transformed into the network weights, and thus we can adaptively optimize these parameters through network training. Then, we employ the DnCNN to develop a denoising module (DnM) and integrate it into the unfolded DNN. Using this DnM, we can enhance the anti-noise ability of the proposed network and obtain a robust DOA estimation in a low SNR. The simulation and experimental results show that the proposed LDnADMM-Net can obtain high-accuracy and super-resolution DOA estimations for a NULA with strong robustness in a low signal-to-noise ratio (SNR). Full article

(This article belongs to the Special Issue State-of-the-Art and Future Developments: Short-Range Radar)

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26 pages, 4633 KiB

Open AccessArticle

A Robust TCPHD Filter for Multi-Sensor Multitarget Tracking Based on a Gaussian–Student’s t-Mixture Model

by Shaoming Wei, Yingbin Lin, Jun Wang, Yajun Zeng, Fangrui Qu, Xuan Zhou and Zhuotong Lu

Remote Sens. 2024, 16(3), 506; https://doi.org/10.3390/rs16030506 - 28 Jan 2024

Viewed by 1174

Abstract

To realize multitarget trajectory tracking under non-Gaussian heavy-tailed noise, we propose a Gaussian–Student t-mixture distribution-based trajectory cardinality probability hypothesis density filter (GSTM-TCPHD). We introduce the multi-sensor GSTM-TCPHD (MS-GSTM-TCPHD) filter to enhance tracking performance. Conventional cardinality probability hypothesis density (CPHD) filters typically assume Gaussian [...] Read more.

To realize multitarget trajectory tracking under non-Gaussian heavy-tailed noise, we propose a Gaussian–Student t-mixture distribution-based trajectory cardinality probability hypothesis density filter (GSTM-TCPHD). We introduce the multi-sensor GSTM-TCPHD (MS-GSTM-TCPHD) filter to enhance tracking performance. Conventional cardinality probability hypothesis density (CPHD) filters typically assume Gaussian noise and struggle to accurately establish target trajectories when faced with heavy-tailed non-Gaussian distributions. Heavy-tailed noise leads to significant estimation errors and filter dispersion. Moreover, the exact trajectory of the target is crucial for tracking and prediction. Our proposed GSTM-TCPHD filter utilizes the GSTM distribution to model heavy-tailed noise, reducing modeling errors and generating a set of potential target trajectories. Since single sensors have a limited field of view and limited measurement information, we extend the filter to a multi-sensor scenario. To tackle the issue of data explosion from multiple sensors, we employed a greedy approximation method to assess measurements and introduced the MS-GSTM-TCPHD filter. The simulation results demonstrate that our proposed filter outperforms the CPHD/TCPHD filter and Student’s t-based TCPHD filter in terms of accurately estimating the trajectories of multiple targets during tracking while also achieving improved accuracy and shorter processing time. Full article

(This article belongs to the Special Issue State-of-the-Art and Future Developments: Short-Range Radar)

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15 pages, 4840 KiB

Open AccessCommunication

ADMM-Net for Beamforming Based on Linear Rectification with the Atomic Norm Minimization

by Zhenghui Gong, Xinyu Zhang, Mingjian Ren, Xiaolong Su and Zhen Liu

Remote Sens. 2024, 16(1), 96; https://doi.org/10.3390/rs16010096 - 25 Dec 2023

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Abstract

Target misalignment can cause beam pointing deviations and degradation of sidelobe performance. In order to eliminate the effect of target misalignment, we formulate the jamming sub-space recovery problem as a linearly modified atomic norm-based optimization. Then, we develop a deep-unfolding network based on [...] Read more.

Target misalignment can cause beam pointing deviations and degradation of sidelobe performance. In order to eliminate the effect of target misalignment, we formulate the jamming sub-space recovery problem as a linearly modified atomic norm-based optimization. Then, we develop a deep-unfolding network based on the alternating direction method of multipliers (ADMM), which effectively improves the applicability and efficiency of the algorithm. By using the back-propagation process of deep-unfolding networks, the proposed method could optimize the hyper-parameters in the original atomic norm. This feature enables the adaptive beamformer to adjust its weight according to the observed data. Specifically, the proposed method could determine the optimal hyper-parameters under different interference noise matrix conditions. Simulation results demonstrate that the proposed network could reduce computational cost and achieve near-optimal performance with low complexity. Full article

(This article belongs to the Special Issue State-of-the-Art and Future Developments: Short-Range Radar)

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24 pages, 13737 KiB

Open AccessArticle

Frequency Domain Imaging Algorithms for Short-Range Synthetic Aperture Radar

by Fatong Zhang, Chenyang Luo, Yaowen Fu, Wenpeng Zhang, Wei Yang, Ruofeng Yu and Shangqu Yan

Remote Sens. 2023, 15(24), 5684; https://doi.org/10.3390/rs15245684 - 11 Dec 2023

Cited by 1 | Viewed by 1295

Abstract

In order to achieve miniaturization, short-range radar (SRR) generally adopts millimeter-wave (MMW) radar with a frequency-modulated continuous-wave (FMCW) system, which may make the stop–go–stop assumption in traditional synthetic aperture radar (SAR) imaging algorithms invalid. In addition, in order to observe a large enough [...] Read more.

In order to achieve miniaturization, short-range radar (SRR) generally adopts millimeter-wave (MMW) radar with a frequency-modulated continuous-wave (FMCW) system, which may make the stop–go–stop assumption in traditional synthetic aperture radar (SAR) imaging algorithms invalid. In addition, in order to observe a large enough area, SRR often needs a wide radar beam, which may cause serious range–azimuth coupling when using SRR for SAR imaging. The above two problems may make the traditional SAR imaging algorithm invalid in SRR SAR imaging. Taking the SRR SAR imaging application into account, traditional frequency domain SAR imaging algorithms are analyzed and improved in this paper. Firstly, the intra-pulse motion (IPM) caused by the FMCW system and the two-dimensional coupling (TDC) in the case of a wide beam are analyzed. Subsequently, the applicability of the range Doppler algorithm (RDA), the frequency scaling algorithm (FSA) and the range migration algorithm (RMA) for SRR SAR is analyzed. Then, improvement measures are put forward to address the aliasing and folding phenomena caused by the wide-beam problem in the FSA and RMA, respectively. Finally, the effectiveness of the proposed algorithm is verified using simulation data and real measured data collected using an MMW radar fixed on a slide rail. Full article

(This article belongs to the Special Issue State-of-the-Art and Future Developments: Short-Range Radar)

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22 pages, 7127 KiB

Open AccessArticle

Generalized Zero-Shot Space Target Recognition Based on Global-Local Visual Feature Embedding Network

by Yuanpeng Zhang, Jingye Guan, Haobo Wang, Kaiming Li, Ying Luo and Qun Zhang

Remote Sens. 2023, 15(21), 5156; https://doi.org/10.3390/rs15215156 - 28 Oct 2023

Cited by 1 | Viewed by 1430

Abstract

Existing deep learning-based space target recognition methods rely on abundantly labeled samples and are not capable of recognizing samples from unseen classes without training. In this article, based on generalized zero-shot learning (GZSL), we propose a space target recognition framework to simultaneously recognize [...] Read more.

Existing deep learning-based space target recognition methods rely on abundantly labeled samples and are not capable of recognizing samples from unseen classes without training. In this article, based on generalized zero-shot learning (GZSL), we propose a space target recognition framework to simultaneously recognize space targets from both seen and unseen classes. First, we defined semantic attributes to describe the characteristics of different categories of space targets. Second, we constructed a dual-branch neural network, termed the global-local visual feature embedding network (GLVFENet), which jointly learns global and local visual features to obtain discriminative feature representations, thereby achieving GZSL for space targets with higher accuracy. Specifically, the global visual feature embedding subnetwork (GVFE-Subnet) calculates the compatibility score by measuring the cosine similarity between the projection of global visual features in the semantic space and various semantic vectors, thereby obtaining global visual embeddings. The local visual feature embedding subnetwork (LVFE-Subnet) introduces soft space attention, and an encoder discovers the semantic-guided local regions in the image to then generate local visual embeddings. Finally, the visual embeddings from both branches were combined and matched with semantics. The calibrated stacking method is introduced to achieve GZSL recognition of space targets. Extensive experiments were conducted on an electromagnetic simulation dataset of nine categories of space targets, and the effectiveness of our GLVFENet is confirmed. Full article

(This article belongs to the Special Issue State-of-the-Art and Future Developments: Short-Range Radar)

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14 pages, 1409 KiB

Open AccessTechnical Note

Limited Sample Radar HRRP Recognition Using FWA-GAN

by Yiheng Song, Liang Zhang and Yanhua Wang

Remote Sens. 2024, 16(16), 2963; https://doi.org/10.3390/rs16162963 - 12 Aug 2024

Viewed by 796

Abstract

In radar High-Resolution Range Profile (HRRP) target recognition, the targets of interest are always non-cooperative, posing a significant challenge in acquiring sufficient samples. This limitation results in the prevalent issue of limited sample availability. To mitigate this problem, researchers have sought to integrate [...] Read more.

In radar High-Resolution Range Profile (HRRP) target recognition, the targets of interest are always non-cooperative, posing a significant challenge in acquiring sufficient samples. This limitation results in the prevalent issue of limited sample availability. To mitigate this problem, researchers have sought to integrate handcrafted features into deep neural networks, thereby augmenting the information content. Nevertheless, existing methodologies for fusing handcrafted and deep features often resort to simplistic addition or concatenation approaches, which fail to fully capitalize on the complementary strengths of both feature types. To address these shortcomings, this paper introduces a novel radar HRRP feature fusion technique grounded in the Feature Weight Assignment Generative Adversarial Network (FWA-GAN) framework. This method leverages the generative adversarial network architecture to facilitate feature fusion in an innovative manner. Specifically, it employs the Feature Weight Assignment Model (FWA) to adaptively assign attention weights to both handcrafted and deep features. This approach enables a more efficient utilization and seamless integration of both feature modalities, thereby enhancing the overall recognition performance under conditions of limited sample availability. As a result, the recognition rate increases by over 4% compared to other state-of-the-art methods on both the simulation and experimental datasets. Full article

(This article belongs to the Special Issue State-of-the-Art and Future Developments: Short-Range Radar)

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17 pages, 3542 KiB

Open AccessTechnical Note

Pseudopolar Format Matrix Description of Near-Range Radar Imaging and Fractional Fourier Transform

by Lilong Zou, Ying Li and Amir M. Alani

Remote Sens. 2024, 16(13), 2482; https://doi.org/10.3390/rs16132482 - 6 Jul 2024

Viewed by 888

Abstract

Near-range radar imaging (NRRI) has evolved into a vital technology with diverse applications spanning fields such as remote sensing, surveillance, medical imaging and non-destructive testing. The Pseudopolar Format Matrix (PFM) has emerged as a promising technique for representing radar data in a compact [...] Read more.

Near-range radar imaging (NRRI) has evolved into a vital technology with diverse applications spanning fields such as remote sensing, surveillance, medical imaging and non-destructive testing. The Pseudopolar Format Matrix (PFM) has emerged as a promising technique for representing radar data in a compact and efficient manner. In this paper, we present a comprehensive PFM description of near-range radar imaging. Furthermore, this paper also explores the integration of the Fractional Fourier Transform (FrFT) with PFM for enhanced radar signal analysis. The FrFT—a powerful mathematical tool for signal processing—offers unique capabilities in analysing signals with time-frequency localization properties. By combining FrFT with PFM, we have achieved significant advancements in radar imaging, particularly in dealing with complex clutter environments and improving target detection accuracy. Meanwhile, this paper highlights the imaging matrix form of FrFT under the PFM, emphasizing the potential for addressing challenges encountered in near-range radar imaging. Finally, numerical simulation and real-world scenario measurement imaging results verify optimized accuracy and computational efficiency with the fusion of PFM and FrFT techniques, paving the way for further innovations in near-range radar imaging applications. Full article

(This article belongs to the Special Issue State-of-the-Art and Future Developments: Short-Range Radar)

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