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Technical Developments in Radar—Processing and Application

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 16796

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Dr. Xiaolong Chen

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Department of Electronic Information Engineering, Naval Aviation University, Yantai 246000, China
Interests: radar signal processing; AI for radar target detection
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Prof. Dr. Shuwen Xu

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Guest Editor

School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: radar; radar signal processing
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Dr. Luca Pallotta

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School of Engineering, University of Basilicata, 85100 Potenza, Italy
Interests: radar; radar signal processing
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Special Issue Information

Dear Colleagues,

With the development of new radar systems such as multi-input-multiple-output (MIMO) radar, frequency diversity array (FDA) radar, networked radar, cognitive radar, ubiquitous radar, distributed radar, passive radar, etc., radar data acquisition methods have gradually evolved from single-band, single-polarization, single-angle, etc., to multi-frequency, multi-polarization, multi-angle acquisitions. The multi-dimensional signal processing for radar is gradually developed to meet the needs of complex radar target detection, parameter estimation, target tracking, and target recognition. Improvement of radar processing capability is the core issue for the application of the novel radar systems. Therefore, the research on signal and data processing, classification, and remote sensing has extremely important theoretical value and practical significance.

The objective of this Special Issue is to provide a forum for radar researchers to present their recent advances in the field. Radar processing (signal, data, and images processing) is closely related to remote sensing.

Suggested themes and article types for submissions.

Radar waveform design and optimization technology;
Radar array processing technology;
Radar polarization processing technology;
Radar anti-interference technology;
Radar target detection technology;
Radar target tracking technology;
Radar target recognition technology;
Radar imaging technology;
AI for radar processing;
Novel radar system and processing.

Dr. Xiaolong Chen
Prof. Dr. Shuwen Xu
Dr. Luca Pallotta
Guest Editors

Manuscript Submission Information

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Keywords

radar detection
radar tracking
radar target recognition
radar signal processing
radar remote sensing
deep learning for radar

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Related Special Issue

Technical Developments in Radar—Processing and Application (2nd Edition) in Remote Sensing (1 article)

Published Papers (12 papers)

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Research

18 pages, 6543 KiB

Open AccessArticle

UAV Swarm Target Identification and Quantification Based on Radar Signal Independency Characterization

by Jia Liu, Qun-Yu Xu, Min Su and Wei-Shi Chen

Remote Sens. 2024, 16(18), 3512; https://doi.org/10.3390/rs16183512 - 21 Sep 2024

Viewed by 1406

Abstract

Radar surveillance of noncooperative UAV swarm is challenging and is involved in many critical surveillance scenarios. The multimodality property of dynamic UAV swarm targets presents larger radar signature complexity and elevates the radar detection difficulty. The swarm unit number ambiguity from dense UAV grouping also inhibits radar monitoring accuracy. Inspired by the coherent integration essence of swarm target signals, this paper proposes a radar signal processing framework based on complex valued independent component analysis (cICA) for swarm target identification and quantification. The target detection threshold is determined from pure clutter signals after cICA processing. A customized clustering algorithm is applied on independent components for swarm target quantification. Target detection and quantification methods are verified with various multimodality UAV swarm flight plans. The results indicate that the detection performance of the proposed method is comparable with conventional CFAR algorithms with better stability performance. The target quantification procedure could estimate swarm unit numbers with acceptable numerical deviations. More discussions are given on the relevance between quantification accuracy and swarm configurations with respect to signal independency mechanisms. Efficiency discussions reveal the bottleneck of the proposed method for future optimization works. Full article

(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)

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19 pages, 31372 KiB

Open AccessArticle

A Target Detection Algorithm Based on Fusing Radar with a Camera in the Presence of a Fluctuating Signal Intensity

by Yanqiu Yang, Xianpeng Wang, Xiaoqin Wu, Xiang Lan, Ting Su and Yuehao Guo

Remote Sens. 2024, 16(18), 3356; https://doi.org/10.3390/rs16183356 - 10 Sep 2024

Viewed by 1097

Abstract

Radar point clouds will experience variations in density, which may cause incorrect alerts during clustering. In turn, it will diminish the precision of the decision-level fusion method. To address this problem, a target detection algorithm based on fusing radar with a camera in the presence of a fluctuating signal intensity is proposed in this paper. It introduces a snow ablation optimizer (SAO) for solving the optimal parameters of the density-based spatial clustering of applications with noise (DBSCAN). Subsequently, the enhanced DBSCAN clusters radar point clouds, and the valid clusters are fused with monocular camera targets. The experimental results indicate that the suggested fusion method can attain a Balance-score ranging from 0.97 to 0.99, performing outstandingly in preventing missed detections and false alarms. Additionally, the fluctuation range of the Balance-score is within 0.02, indicating the algorithm has an excellent robustness. Full article

(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)

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22 pages, 5847 KiB

Open AccessArticle

Intelligent Tracking Method for Aerial Maneuvering Target Based on Unscented Kalman Filter

by Yunlong Dong, Weiqi Li, Dongxue Li, Chao Liu and Wei Xue

Remote Sens. 2024, 16(17), 3301; https://doi.org/10.3390/rs16173301 - 5 Sep 2024

Viewed by 1082

Abstract

This paper constructs a nonlinear iterative filtering framework based on a neural network prediction model. It uses recurrent neural networks (RNNs) to achieve accurate regression of complex maneuvering target dynamic models and integrates them into the nonlinear iterative filtering system via Unscented Transformation (UT). In constructing the neural network prediction model, the Temporal Convolutional Network (TCN) modules that capture long-term dependencies and the Long Short-Term Memory (LSTM) modules that selectively forget non-essential information were utilized to achieve accurate regression of the maneuvering models. When embedding the neural network prediction model, this paper proposes a method for extracting Sigma points using the UT transformation by ‘unfolding’ multi-sequence vectors and explores design techniques for the time sliding window length of recurrent neural networks. Ultimately, an intelligent tracking algorithm based on unscented filtering, called TCN-LSTM-UKF, was developed, effectively addressing the difficulties of constructing models and transition delays under high-maneuvering conditions and significantly improving the tracking performance of highly maneuvering targets. Full article

(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)

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18 pages, 6860 KiB

Open AccessArticle

Weak Target Detection Based on Full-Polarization Scattering Features under Sea Clutter Background

by Yifei Fan, Duo Chen, Shichao Chen, Jia Su, Mingliang Tao, Zixun Guo and Ling Wang

Remote Sens. 2024, 16(16), 2987; https://doi.org/10.3390/rs16162987 - 14 Aug 2024

Cited by 2 | Viewed by 955

Abstract

Aiming at the low observable target detection under sea clutter backgrounds, this paper emphasizes the exploration of distinguishable full-polarization features between target and sea clutter echoes. To overcome the shortcomings of the existing polarization feature-based methods, the full-polarization features of sea clutter are modeled and analyzed in detail by using Van Zyl polarization decomposition. Then, three polarimetric features (the relative surface scattering energy, the relative dihedral scattering energy and the relative diffuse scattering energy) are extracted from the fully polarimetric radar sea clutter echoes, which improve the feature differences between sea clutter and targets. And a tri-polarimetric feature detector with constant false alarm rate (CFAR) is constructed based on the fast convex hull learning algorithm. The experimental results on the real measured IPIX radar datasets prove that the proposed full-polarization feature detector obtains more competitive detection performance and lower computational complexity than the several existing feature-based detectors. Full article

(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)

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22 pages, 4522 KiB

Open AccessArticle

Compound-Gaussian Clutter Model with Weibull-Distributed Textures and Parameter Estimation

by Pengjia Zou, Siyuan Chang and Penglang Shui

Remote Sens. 2024, 16(16), 2912; https://doi.org/10.3390/rs16162912 - 9 Aug 2024

Viewed by 991

Abstract

Compound-Gaussian models (CGMs) are widely used to characterize sea clutter. Various types of texture distributions have been developed so that the CGMs can cover sea clutter in different conditions. In this paper, the Weibull distributions are used to model textures of sea clutter, and the CGM with Weibull-distributed textures is used to derive the CGWB distributions, a new type of biparametric distribution. Like the classic K-distributions and Compound-Gaussian with lognormal texture (CGLN) distributions, the biparametric CGWB distributions without analytical expressions can be represented by the closed-form improper integral. Further, the properties of the CGWB distributions are investigated, and four moment-based estimators using sample moments, fractional-order sample moments, and generalized sample moments are given to estimate the parameters of the CGWB distributions. Their performance is compared by simulated clutter data. Moreover, measured sea clutter data are used to examine the suitability of the CGWB distributions. The results show that the CGWB distributions can provide the best goodness-of-the-fit for low-resolution sea clutter data as alternatives to the classic K-distributions. Full article

(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)

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24 pages, 10066 KiB

Open AccessArticle

A Small Maritime Target Detection Method Using Nonlinear Dimensionality Reduction and Feature Sample Distance

by Jian Guan, Xingyu Jiang, Ningbo Liu, Hao Ding, Yunlong Dong and Zhongping Guo

Remote Sens. 2024, 16(16), 2901; https://doi.org/10.3390/rs16162901 - 8 Aug 2024

Viewed by 1156

Abstract

Addressing the challenge of radar detection of small targets under sea clutter, target detection methods based on a three-dimensional feature space have shown effectiveness. However, their application has revealed several problems, including high dependency on linear relationships between features for dimensionality reduction, unclear reduction objectives, and spatial divergence of target samples, which limit detection performance. To mitigate these challenges, we constructed a feature density distance metric employing copula functions to quantitatively describe the classification capability of multidimensional features to distinguish targets from sea clutter. On the basis of this, a lightweight nonlinear dimensionality reduction network utilizing a self-attention mechanism was developed, optimally re-expressing multidimensional features into a three-dimensional feature space. Additionally, a concave hull classifier using feature sample distance was proposed to mitigate the negative impact of target sample divergence in the feature space. Furthermore, multivariate autoregressive prediction was used to optimize features, reducing erroneous decisions caused by anomalous feature samples. Experimental results using the measured data from the SDRDSP public dataset demonstrated that the proposed detection method achieved a detection probability more than 4% higher than comparative methods under Sea State 5, was less affected by false alarm rates, and exhibited superior detection performance under different false alarm probabilities from 10⁻³ to 10⁻¹. Full article

(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)

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20 pages, 31178 KiB

Open AccessArticle

TC–Radar: Transformer–CNN Hybrid Network for Millimeter-Wave Radar Object Detection

by Fengde Jia, Chenyang Li, Siyi Bi, Junhui Qian, Leizhe Wei and Guohao Sun

Remote Sens. 2024, 16(16), 2881; https://doi.org/10.3390/rs16162881 - 7 Aug 2024

Cited by 2 | Viewed by 2463

Abstract

In smart transportation, assisted driving relies on data integration from various sensors, notably LiDAR and cameras. However, their optical performance can degrade under adverse weather conditions, potentially compromising vehicle safety. Millimeter-wave radar, which can overcome these issues more economically, has been re-evaluated. Despite this, developing an accurate detection model is challenging due to significant noise interference and limited semantic information. To address these practical challenges, this paper presents the TC–Radar model, a novel approach that synergistically integrates the strengths of transformer and the convolutional neural network (CNN) to optimize the sensing potential of millimeter-wave radar in smart transportation systems. The rationale for this integration lies in the complementary nature of CNNs, which are adept at capturing local spatial features, and transformers, which excel at modeling long-range dependencies and global context within data. This hybrid approach allows for a more robust and accurate representation of radar signals, leading to enhanced detection performance. A key innovation of our approach is the introduction of the Cross-Attention (CA) module, which facilitates efficient and dynamic information exchange between the encoder and decoder stages of the network. This CA mechanism ensures that critical features are accurately captured and transferred, thereby significantly improving the overall network performance. In addition, the model contains the dense information fusion block (DIFB) to further enrich the feature representation by integrating different high-frequency local features. This integration process ensures thorough incorporation of key data points. Extensive tests conducted on the CRUW and CARRADA datasets validate the strengths of this method, with the model achieving an average precision (AP) of 83.99% and a mean intersection over union (mIoU) of 45.2%, demonstrating robust radar sensing capabilities. Full article

(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)

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24 pages, 8278 KiB

Open AccessArticle

Radar Target Classification Using Enhanced Doppler Spectrograms with ResNet34_CA in Ubiquitous Radar

by Qiang Song, Shilin Huang, Yue Zhang, Xiaolong Chen, Zebin Chen, Xinyun Zhou and Zhenmiao Deng

Remote Sens. 2024, 16(15), 2860; https://doi.org/10.3390/rs16152860 - 5 Aug 2024

Viewed by 1294

Abstract

Ubiquitous Radar has become an essential tool for preventing bird strikes at airports, where accurate target classification is of paramount importance. The working mode of Ubiquitous Radar, which operates in track-then-identify (TTI) mode, provides both tracking information and Doppler information for the classification and recognition module. Moreover, the main features of the target’s Doppler information are concentrated around the Doppler main spectrum. This study innovatively used tracking information to generate a feature enhancement layer that can indicate the area where the main spectrum is located and combines it with the RGB three-channel Doppler spectrogram to form an RGBA four-channel Doppler spectrogram. Compared with the RGB three-channel Doppler spectrogram, this method increases the classification accuracy for four types of targets (ships, birds, flapping birds, and bird flocks) from 93.13% to 97.13%, an improvement of 4%. On this basis, this study integrated the coordinate attention (CA) module into the building block of the 34-layer residual network (ResNet34), forming ResNet34_CA. This integration enables the network to focus more on the main spectrum information of the target, thereby further improving the classification accuracy from 97.13% to 97.22%. Full article

(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)

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22 pages, 937 KiB

Open AccessArticle

Radar Emitter Recognition Based on Spiking Neural Networks

by Zhenghao Luo, Xingdong Wang, Shuo Yuan and Zhangmeng Liu

Remote Sens. 2024, 16(14), 2680; https://doi.org/10.3390/rs16142680 - 22 Jul 2024

Cited by 2 | Viewed by 1300

Abstract

Efficient and effective radar emitter recognition is critical for electronic support measurement (ESM) systems. However, in complex electromagnetic environments, intercepted pulse trains generally contain substantial data noise, including spurious and missing pulses. Currently, radar emitter recognition methods utilizing traditional artificial neural networks (ANNs) like CNNs and RNNs are susceptible to data noise and require intensive computations, posing challenges to meeting the performance demands of modern ESM systems. Spiking neural networks (SNNs) exhibit stronger representational capabilities compared to traditional ANNs due to the temporal dynamics of spiking neurons and richer information encoded in precise spike timing. Furthermore, SNNs achieve higher computational efficiency by performing event-driven sparse addition calculations. In this paper, a lightweight spiking neural network is proposed by combining direct coding, leaky integrate-and-fire (LIF) neurons, and surrogate gradients to recognize radar emitters. Additionally, an improved SNN for radar emitter recognition is proposed, leveraging the local timing structure of pulses to enhance adaptability to data noise. Simulation results demonstrate the superior performance of the proposed method over existing methods. Full article

(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)

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21 pages, 20321 KiB

Open AccessArticle

Spatial–Temporal Joint Design and Optimization of Phase-Coded Waveform for MIMO Radar

by Wei Lei, Yue Zhang, Zengping Chen, Xiaolong Chen and Qiang Song

Remote Sens. 2024, 16(14), 2647; https://doi.org/10.3390/rs16142647 - 19 Jul 2024

Cited by 1 | Viewed by 978

Abstract

By simultaneously transmitting multiple different waveform signals, a multiple-input multiple-output (MIMO) radar possesses higher degrees of freedom and potential in many aspects compared to a traditional phased-array radar. The spatial–temporal characteristics of waveforms are the key to determining their performance. In this paper, a transmitting waveform design method based on spatial–temporal joint (STJ) optimization for a MIMO radar is proposed, where waveforms are designed not only for beam-pattern matching (BPM) but also for minimizing the autocorrelation sidelobes (ACSLs) of the spatial synthesis signals (SSSs) in the directions of interest. Firstly, the STJ model is established, where the two-step strategy and least squares method are utilized for BPM, and the L2p-Norm of the ACSL is constructed as the criterion for temporal characteristics optimization. Secondly, by transforming it into an unconstrained optimization problem about the waveform phase and using the gradient descent (GD) algorithm, the hard, non-convex, high-dimensional, nonlinear optimization problem is solved efficiently. Finally, the method’s effectiveness is verified through numerical simulation. The results show that our method is suitable for both orthogonal and partial-correlation MIMO waveform designs and efficiently achieves better spatial–temporal characteristic performances simultaneously in comparison with existing methods. Full article

(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)

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22 pages, 19485 KiB

Open AccessArticle

A Hybrid Integration Method Based on SMC-PHD-TBD for Multiple High-Speed and Highly Maneuverable Targets in Ubiquitous Radar

by Zebin Chen, Xiangyu Peng, Junyao Yang, Zhanming Zhong, Qiang Song and Yue Zhang

Remote Sens. 2024, 16(14), 2618; https://doi.org/10.3390/rs16142618 - 17 Jul 2024

Viewed by 861

Abstract

Based on the characteristic of ubiquitous radar emitting low-gain wide beam, a method of long-time coherent integration (LTCI) is required to enhance target detection capability. However, high-speed and highly maneuverable targets can cause Doppler frequency migration (DFM), range migration (RM), and velocity ambiguity (VA), severely degrading the performance of LTCI. Additionally, the number of targets is unknown and variable, and the presence of clutter further complicates the target tracking problem. To address these challenges, we propose a hybrid integration method to achieve joint detection and estimation of multiple high-speed, and highly maneuverable targets. Firstly, we compensate for first-order RM using the keystone transform (KT) and generate corresponding sub-range-Doppler (SRD) planes with different folding factors to achieve VA compensation. These SRD planes are then stitched together to form an extended range-Doppler (ERD) plane, which covers a broader velocity range. Secondly, during the track-before-detect (TBD) process, tracking is performed directly on the ERD plane. We use the sequential Monte Carlo (SMC) approximation of the probability hypothesis density (PHD) to propagate multi-target states. Additionally, we propose an amplitude-based adaptive prior distribution method and a line spread model (LSM) observation model to compensate for DFM. Since the acceleration of the target is included in the particle state, using particles to search for DFM does not increase the computational load. To address the issue of misclassifying mirror targets as real targets in the SRD plane, we propose a particle space projection method. By stacking the SRD planes to create a folding range-Doppler (FRD) space, particles are projected along the folding factor dimension, and then, the particles are clustered to eliminate the influence of the mirror targets. Finally, through simulation experiments, the superiority of the LSM for targets with acceleration was demonstrated. In comparative experiments, the proposed method showed superior performance and robustness compared to traditional methods, achieving a balance between performance and computational efficiency. Furthermore, the proposed method’s capability to detect and track multiple high-speed and highly maneuverable targets was validated using actual data from a ubiquitous radar system. Full article

(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)

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24 pages, 6459 KiB

Open AccessArticle

An Efficient Ground Moving Target Imaging Method for Synthetic Aperture Radar Based on Scaled Fourier Transform and Scaled Inverse Fourier Transform

by Xin Zhang, Haoyu Zhu, Ruixin Liu, Jun Wan and Zhanye Chen

Remote Sens. 2024, 16(11), 2039; https://doi.org/10.3390/rs16112039 - 6 Jun 2024

Viewed by 825

Abstract

The unknown relative motions between synthetic aperture radar (SAR) and a ground moving target will lead to serious range cell migration (RCM) and Doppler frequency spread (DFS). The energy of the moving target will defocus, given the effect of the RCM and DFS. The moving target will easily produce Doppler ambiguity, due to the low pulse repetition frequency of radar, and the Doppler ambiguity complicates the corrections of the RCM and DFS. In order to address these issues, an efficient ground moving target focusing method for SAR based on scaled Fourier transform and scaled inverse Fourier transform is presented. Firstly, the operations based on the scaled Fourier transform and scaled inverse Fourier transforms are presented to focus the moving targets in consideration of Doppler ambiguity. Subsequently, in accordance with the detailed analysis of multiple target focusing, the spurious peak related to the cross term is removed. The proposed method can accurately eliminate the DFS and RCM, and the well-focused result of the moving target can be achieved under the complex Doppler ambiguity. Then, the blind speed sidelobe can be further avoided. The presented method has high computational efficiency without the step of parameter search. The simulated and measured SAR data are provided to demonstrate the effectiveness of the developed method. Full article

(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)

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