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State-of-the-Art and Future Developments: Short-Range Radar

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 5322

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Special Issue Editors

Dr. Yanhua Wang

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Guest Editor

Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: radar

Dr. Liang Zhang

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Guest Editor

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: short range/high resolution radar; radar polarization; target detection and recognition

Dr. Shunqiao Sun

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Guest Editor

Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
Interests: interface of statistical and sparse signal processing with mathematical optimizations; MIMO radar; machine learning

Dr. Pu Wang

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Guest Editor

Mitsubishi Electric Research Laboratories, Cambridge, MA 02139, USA
Interests: signal processing; communications, computational sensing; machine learning

Special Issue Information

Dear Colleagues,

Short-range radar (SRR) has emerged as an essential technology in various applications, including automotive safety, industrial automation, healthcare, robotics, and security systems. The ability to detect and analyze objects with high resolution and accuracy has contributed to the tremendous growth of SRR. Recent technological advancements in SRR, such as modern signal processing, miniaturization, cost reduction, and integration with other sensors, have granted unprecedented capabilities. These developments present both exciting opportunities and challenges for scientists, engineers, and practitioners working in the field of SRR.

The present landscape of SRR calls for a consolidated effort to understand the current state of the art and to chart out the future directions. Therefore, this Special Issue aims to bring together original research articles and reviews that delve into the complexities, innovations, and potential of short-range radar.

Potential topics for this Special Issue include, but are not limited to:

Recent advancements in SRR design and optimization for diverse applications;
Signal processing, imaging, and data interpretation techniques specific to SRR;
Integration of SRR with other sensing modalities and systems;
Novel detection and tracking models using multi-modal information (e.g. SRR, infrared, visible light. etc.) in remote sensing ;
Challenges and solutions in SRR deployment in real-world scenarios;
The impact of AI and machine learning in enhancing SRR's capabilities;
Other emerging trends and novel methodologies in SRR research and development.

Dr. Yanhua Wang
Dr. Liang Zhang
Dr. Shunqiao Sun
Dr. Pu Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

short-range radar
object detection, recognition and tracking
radar imaging
sensor integration and data fusion
signal processing

Published Papers (7 papers)

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Research

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

Viewed by 314

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 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

Viewed by 374

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 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 1 | Viewed by 650

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 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 669

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 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

Viewed by 691

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 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

Viewed by 739

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 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

Viewed by 963

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 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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