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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 21334

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Special Issue Editors

Dr. Weike Feng

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Guest Editor

Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China
Interests: space-time adaptive processing; passive bistatic radar; MIMO radar; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaowei Hu

E-Mail Website
Guest Editor

Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China
Interests: MIMO radar imaging; SAR target recognition; deep learning; cognitive radar

Dr. Xingyu He

E-Mail Website
Guest Editor

Air Traffic Control and Navigation College, Air Force Engineering University, Xi’an 710051, China
Interests: ISAR imaging; target classification; deep learning; compressive sensing

Special Issue Information

Dear Colleagues,

With the all-day and all-weather working capacity, radar is currently widely used in many civilian and military applications, e.g., autonomous driving, medical health monitoring, geological survey, and ground/sea/air/space surveillance. In these various applications, the core for radar to reveal the information of targets (e.g., humans, land surface, cars, and aircraft) is related to signal processing and imaging methods. In the last few decades, the theory and methodology of radar signal processing and radar imaging have made considerable progress. In particular, with the recent breakthrough of artificial intelligence (AI), especially deep learning, many innovative approaches have been proposed for radio-frequency interference recognition, ground/sea clutter suppression, moving target detection, direction-of-arrival (DOA) estimation, as well as high-resolution target imaging via synthetic aperture radar (SAR), inverse SAR (ISAR), and multiple-input-multiple-output (MIMO) radar, to name a few.

This Special Issue aims to gather the latest research results in the area of radar signal processing and radar imaging, with an emphasis on AI-based methods. We invite researchers to contribute original research articles and comprehensive review articles. Topics include but are not limited to:

Radar array signal processing;
Radar target detection, estimation, and tracking;
Radar jamming and clutter suppression;
Radar waveform design and optimization;
Radar classification, identification, and recognition;
SAR and ISAR imaging;
MIMO radar imaging;
Passive bistatic radar imaging;
Microwave coincidence imaging;
Through-the-wall radar and ground-penetrating radar imaging;
AI-based radar signal processing and radar imaging techniques.

Dr. Weike Feng
Dr. Xiaowei Hu
Dr. Xingyu He
Guest Editors

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Keywords

radar signal processing
radar imaging
artificial intelligence
machine learning
deep learning
neural networks

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Published Papers (13 papers)

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Editorial

Jump to: Research

6 pages, 164 KiB

Open AccessEditorial

Artificial Intelligence (AI)-Based Radar Signal Processing and Radar Imaging

by Weike Feng, Xiaowei Hu and Xingyu He

Electronics 2024, 13(21), 4251; https://doi.org/10.3390/electronics13214251 - 30 Oct 2024

Viewed by 1302

Abstract

In recent years, artificial intelligence (AI), especially deep learning, has led to remarkable achievements in image recognition, speech recognition, autonomous driving and many other fields [...] Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Signal Processing and Radar Imaging)

Research

Jump to: Editorial

20 pages, 3866 KiB

Open AccessArticle

Millimeter-Wave Radar Clutter Suppression Based on Cycle-Consistency Generative Adversarial Network

by Ziyi Li, Yang Li, Yanping Wang, Tong Zheng and Hongquan Qu

Electronics 2024, 13(21), 4166; https://doi.org/10.3390/electronics13214166 - 23 Oct 2024

Viewed by 700

Abstract

Vehicle-mounted millimeter-wave radar is widely used in autonomous driving systems for its ability to observe road scenes at all times and in all weathers. However, the data collected by millimeter-wave radar are seriously affected by the existence of clutter. This clutter will result in false detection during object detection. To address this issue, a feature extraction network with clutter suppression is necessary. This paper proposes a new clutter suppression method for millimeter-wave Range–Angle (RA) images based on a cycle-consistency generative adversarial network (CycleGAN). The generator of the method can be used as the feature extraction network of the object detection. The method aims to convert cluttered images into clutter-free images by unsupervised learning. In this method, an attention gate (AG) is introduced into the generator, a spatial attention mechanism that improves the ability of the model to automatically learn to focus on the features of targets and suppress the clutter of the background. Additionally, the target consistency loss term is added to the loss function to maintain target integrity while suppressing network training overfitting. The public dataset CRUW is utilized to evaluate the performance of the proposed method, which is compared and analyzed with traditional methods and deep learning methods. Experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the proposed method reach 39.846 and 0.990, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Signal Processing and Radar Imaging)

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19 pages, 2764 KiB

Open AccessArticle

A Fast Phase-Only Beamforming Algorithm for FDA-MIMO Radar via Kronecker Decomposition

by Geng Chen , Chunyang Wang , Jian Gong and Ming Tan

Electronics 2024, 13(2), 337; https://doi.org/10.3390/electronics13020337 - 12 Jan 2024

Viewed by 1027

Abstract

This paper proposes a fast phase-only beamforming algorithm for frequency diverse array multiple-input multiple-output radar systems. Specifically, we use the Kronecker decomposition to decompose the desired phase-only weight vector into phase-only transmit and receive weight vectors and to decompose the target steering vector into transmit and receive steering vectors. By using the properties of the Kronecker product, the transmit and receive steering vectors and the transmit and receive weight vectors with the Vandermonde structure are decomposed into Kronecker factors with uni-modulus vectors, respectively. On this basis, in order to maintain the mainlobe gain and form a deep null at the desired position, the Kronecker factors are divided into two parts.The first component, referred to as the interference suppression factors, is responsible for creating deep nulls. The second component, known as the signal enhancement factor, maintains the mainlobe gain. We provide an analytical solution with low complexity for the Kronecker factors. This strategy can obtain the phase-only weights while effectively forming a deep null at the desired position. Numerical experiments are conducted to verify the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Signal Processing and Radar Imaging)

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18 pages, 15210 KiB

Open AccessArticle

An SAR Imaging and Detection Model of Multiple Maritime Targets Based on the Electromagnetic Approach and the Modified CBAM-YOLOv7 Neural Network

by Peng Peng, Qingkuan Wang, Weike Feng, Tong Wang and Chuangming Tong

Electronics 2023, 12(23), 4816; https://doi.org/10.3390/electronics12234816 - 28 Nov 2023

Cited by 1 | Viewed by 1191

Abstract

This paper proposes an Synthetic Aperture Radar (SAR) imaging and detection model of multiple targets at the maritime scene. The sea surface sample is generated according to the composite rough surface theory. The SAR imaging model is constructed based on a hybrid EM calculation approach with the fast ray tracing strategy and the modified facet Small Slope Approximation (SSA) solution. Numerical simulations calculate the EM scattering and the SAR imaging of the multiple cone targets above the sea surface, with the scattering mechanisms analyzed and discussed. The SAR imaging datasets are then set up by the SAR image simulations. A modified YOLOv7 neural network with the Spatial Pyramid Pooling Fast Connected Spatial Pyramid Convolution (SPPFCSPC) module, Convolutional Block Attention Module (CBAM), modified Feature Pyramid Network (FPN) structure and extra detection head is developed. In the training process on our constructed SAR datasets, the precision rate, recall rate, [email protected] and [email protected]:0.95 are 97.46%, 90.08%, 92.91% and 91.98%, respectively, after 300 rounds of training. The detection results show that the modified YOLOv7 has a good performance in selecting the targets out of the complex sea surface and multipath interference background. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Signal Processing and Radar Imaging)

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17 pages, 13338 KiB

Open AccessArticle

A Fast Facet-Based SAR Imaging Model and Target Detection Based on YOLOv5 with CBAM and Another Detection Head

by Qingkuan Wang, Jing Sheng, Chuangming Tong, Zhaolong Wang, Tao Song, Mengdi Wang and Tong Wang

Electronics 2023, 12(19), 4039; https://doi.org/10.3390/electronics12194039 - 26 Sep 2023

Cited by 5 | Viewed by 1650

Abstract

Synthetic Aperture Radar (SAR) image target detection is of great significance in civil surveillance and military reconnaissance. However, there are few publicly released SAR image datasets of typical non-cooperative targets. Aiming to solve this problem, a fast facet-based SAR imaging model is proposed to simulate the SAR images of non-cooperative aircraft targets under different conditions. Combining the iterative physical optics and the Kirchhoff approximation, the scattering coefficient of each facet on the target and rough surface can be obtained. Then, the radar echo signal of an aircraft target above a rough surface environment can be generated, and the SAR images can be simulated under different conditions. Finally, through the simulation experiments, a dataset of typical non-cooperative targets can be established. Combining the YOLOv5 network with the convolutional block attention module (CBAM) and another detection head, a SAR image target detection model based on the established dataset is realized. Compared with other YOLO series detectors, the simulation results show a significant improvement in precision. Moreover, the automatic target recognition system presented in this paper can provide a reference for the detection and recognition of non-cooperative aircraft targets and has great practical application in situational awareness of battlefield conditions. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Signal Processing and Radar Imaging)

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18 pages, 7438 KiB

Open AccessArticle

Echo Preprocessing-Based Smeared Spectrum Interference Suppression

by Xiaoge Wang, Hui Chen, Weijian Liu, Liang Zhang, Binbin Li and Mengyu Ni

Electronics 2023, 12(17), 3690; https://doi.org/10.3390/electronics12173690 - 31 Aug 2023

Cited by 5 | Viewed by 1112

Abstract

Self-protection deceptive interferences (SPDI) are widely used in electronic countermeasures. Smeared spectrum (SMSP) interference, as a typical SPDI, can form a large number of dense false targets at the receiver output to affect effective target detection. Therefore, the suppression of SMSP interference is a compelling issue. The existing SMSP interference suppression methods inevitably result in energy loss of the target due to signal processing. This paper proposes a novel interference suppression method based on echo preprocessing to address this problem. Firstly, the pulse compression (PC) and the coherent integration (CI) characteristics of SMSP interference in the pulse Doppler radar are obtained through the derivation of formulas. Then, echo preprocessing is introduced, and the steps of interference suppression are listed in detail. Finally, the SMSP interference is suppressed because the preprocessed interference forms a center-shifting and range-scaling in the distance dimension after PC, and CI gain cannot be further obtained. The proposed method does not lose the energy of the true target because it does not involve filtering and reconstruction processing. Simulations show that the target detection probability of the proposed method can reach 100% via peak search after the interference suppression when the signal-to-noise ratio is greater than −10 dB and the jamming-to-signal ratio (JSR) is less than 35 dB. Compared with three representative methods in the recent literature, the proposed method has better robustness and higher JSR tolerance. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Signal Processing and Radar Imaging)

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14 pages, 8518 KiB

Open AccessArticle

Analysis of Characteristics and Suppression Methods for Self-Defense Smart Noise Jamming

by Yongzhe Zhu, Zhaojian Zhang, Binbin Li, Bilei Zhou, Hao Chen and Yongliang Wang

Electronics 2023, 12(15), 3270; https://doi.org/10.3390/electronics12153270 - 30 Jul 2023

Cited by 3 | Viewed by 1205

Abstract

Self-defense smart noise jamming can automatically aim a signal frequency and obtain antenna gain and matching filtering processing gain, posing a huge threat to the normal performance of radar. In response to this situation, this article conducts an in-depth analysis of two typical smart noise jamming methods: noise convolution jamming methods and noise product jamming methods. The distribution characteristics of the two jamming methods in both time–frequency dimensions and their internal time–frequency relationships are analyzed. Based on this, a self-defense smart noise jamming suppression method based on pulse frequency stepping is proposed. This method obtains the true distance information of the target based on the phase difference caused by frequency stepping between adjacent pulses and uses this information to construct a filter to filter the radar echo, achieving jamming suppression. Simulation experiments have verified the effectiveness of this method. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Signal Processing and Radar Imaging)

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19 pages, 7267 KiB

Open AccessArticle

Airborne Radar STAP Method Based on Deep Unfolding and Convolutional Neural Networks

by Bo Zou, Weike Feng and Hangui Zhu

Electronics 2023, 12(14), 3140; https://doi.org/10.3390/electronics12143140 - 19 Jul 2023

Cited by 4 | Viewed by 1458

Abstract

The lack of independent and identically distributed (IID) training range cells is one of the key factors that limit the performance of conventional space-time adaptive processing (STAP) methods for airborne radar. Sparse recovery (SR)-based and convolutional neural network (CNN)-based STAP methods can obtain high-resolution estimations of the clutter space-time spectrum by using few IID training range cells, so as to realize the clutter suppression effectively. However, the performance of SR-STAP methods usually depends on the SR algorithms, having the problems of parameter setting difficulty, high computational complexity and low accuracy, and the CNN-STAP methods have a high requirement for the nonlinear mapping capability of CNN. To solve these problems, CNNs can be used to reduce the requirements of SR algorithms for parameter setting and iterations, increasing its accuracy, and the clutter space-time spectrum obtained by SR can be used to reduce the network scale of the CNN, resulting in the method proposed in this paper. Based on the idea of deep unfolding (DU), the SR algorithm is unfolded into a deep neural network, whose optimal parameters are obtained by training to improve its convergence performance. On this basis, the SR network and CNN are trained end-to-end to estimate the clutter space-time spectrum efficiently and accurately. The simulation and experimental results show that, compared to the SR-STAP and CNN-STAP methods, the proposed method can improve the clutter suppression performance and have a lower computational complexity. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Signal Processing and Radar Imaging)

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11 pages, 3435 KiB

Open AccessCommunication

Wideband DOA Estimation Utilizing a Hierarchical Prior Based on Variational Bayesian Inference

by Ninghui Li, Xiaokuan Zhang, Binfeng Zong, Fan Lv, Jiahua Xu and Zhaolong Wang

Electronics 2023, 12(14), 3074; https://doi.org/10.3390/electronics12143074 - 14 Jul 2023

Viewed by 1062

Abstract

The direction-of-arrival (DOA) estimation of wideband signals, based on sparse signal reconstruction, has recently been proposed, owing to its unique high-resolution performance. As a typical tool of sparse signal reconstruction, sparse Bayesian learning (SBL) enhances little sparsity in most works, leading to a non-robust local fitting. To significantly enhance sparsity, we proposed a novel hierarchical Bayesian prior framework, and deduced a novel iterative approach. It was discovered that the iterative approach had a lower computational complexity than the majority of current state-of-the-art algorithms. Besides, the proposed approach achieves a high angular estimation accuracy and sparsity performance, by utilizing the joint sparsity of the multiple measurement vector (MMV) models. Moreover, the approach stabilizes the estimated values between different frequencies or snapshots, so as to obtain a flat spatial spectrum. Extensive simulation results are presented, to demonstrate the superior performance of our method. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Signal Processing and Radar Imaging)

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10 pages, 6152 KiB

Open AccessCommunication

An Algorithm for Sorting Staggered PRI Signals Based on the Congruence Transform

by Huixu Dong, Xiaofeng Wang, Xinglong Qi and Chunyu Wang

Electronics 2023, 12(13), 2888; https://doi.org/10.3390/electronics12132888 - 30 Jun 2023

Cited by 4 | Viewed by 1170

Abstract

To address the problems of poor adaptability to pulse loss, susceptibility to interfered pulses, and the need of sub-PRI (Pulse Repetition Interval) ranking in the existing signal sorting algorithms, this paper proposes an algorithm for sorting staggered PRI signals based on the congruence transform. According to the analysis of the congruence characteristics of the staggered PRI signal, the proposed algorithm transforms the arrival time of the pulse to a fixed value, based on which the staggered PRI signal sorting and the sub-PRI sequence extraction can be achieved. Simulation results show that the proposed algorithm can effectively sort the staggered PRI signals and obtain the sub-PRI sequence directly without sub-PRI ranking, and, compared to some typical algorithms, it is less affected by the interfered pulses and the pulse loss. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Signal Processing and Radar Imaging)

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15 pages, 3566 KiB

Open AccessArticle

Robust Adaptive Beamforming Based on a Convolutional Neural Network

by Zhipeng Liao, Keqing Duan, Jinjun He, Zizhou Qiu and Binbin Li

Electronics 2023, 12(12), 2751; https://doi.org/10.3390/electronics12122751 - 20 Jun 2023

Cited by 4 | Viewed by 2390

Abstract

To address the advancements in jamming technology, it is imperative to consider robust adaptive beamforming (RBF) methods with finite snapshots and gain/phase (G/P) errors. This paper introduces an end-to-end RBF approach that utilizes a two-stage convolutional neural network. The first stage includes convolutional blocks and residual blocks without downsampling; the blocks assess the covariance matrix precisely using finite snapshots. The second stage maps the first stage’s output to an adaptive weight vector employing a similar structure to the first stage. The two stages are pre-trained with different datasets and fine-tuned as end-to-end networks, simplifying the network training process. The two-stage structure enables the network to possess practical physical meaning, allowing for satisfying performance even with a few snapshots in the presence of array G/P errors. We demonstrate the resulting beamformer’s performance with numerical examples and compare it to various other adaptive beamformers. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Signal Processing and Radar Imaging)

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16 pages, 3885 KiB

Open AccessArticle

Joint Power and Bandwidth Allocation in Collocated MIMO Radar Based on the Quality of Service Framework

by Jieyu Huang, Ziqing Yang, Junwei Xie, Haowei Zhang and Zhengjie Li

Electronics 2023, 12(12), 2567; https://doi.org/10.3390/electronics12122567 - 6 Jun 2023

Cited by 2 | Viewed by 1332

Abstract

The simultaneous multi-beam working mode of the collocated multiple-input and multiple-output (MIMO) radar enables the radar to track multiple targets simultaneously. A joint power and bandwidth allocation algorithm in a collocated MIMO radar based on the quality of service (QoS) framework is proposed for the multi-target tracking problem with different threat levels. Firstly, a posterior Cramer–Rao lower bound (PCRLB) concerning the power and bandwidth is derived. In addition, the optimal objective functions of power and bandwidth are designed based on the QoS framework, and the problem is solved using the convex relaxation technique and the cyclical minimization algorithm. The numerical results show that the proposed algorithm has better tracking accuracy and achieves more reasonable resource allocation compared to strategies such as average allocation. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Signal Processing and Radar Imaging)

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14 pages, 3121 KiB

Open AccessArticle

A Method for Suppressing False Target Jamming with Non-Uniform Stepped-Frequency Radar

by Yongzhe Zhu, Zhaojian Zhang, Xiaoge Wang, Binbin Li, Weijian Liu and Hao Chen

Electronics 2023, 12(11), 2534; https://doi.org/10.3390/electronics12112534 - 4 Jun 2023

Cited by 3 | Viewed by 1396

Abstract

Stepped-frequency radar can increase the degree of freedom of the range dimension by adding a tiny stepping frequency between neighboring pulse carrier frequencies, which has a clear advantage in countering range false target jamming. However, when the jamming is released by a self-defense jammer carried by the target, the range information is coupled to the Doppler frequency. This makes it impossible for a stepped-frequency radar to extract the range information accurately. In this paper, we derive the correlation between the phase difference of adjacent pulses and range information and the Doppler frequency when the frequency is uniformly stepped, as well as the error caused by the Doppler frequency in range estimation. Then, we propose a decoupling method based on a waveform design and the corresponding suppression method of range false target jamming. Simulation results show that the proposed method can effectively suppress the jamming of self-defense range false targets. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Signal Processing and Radar Imaging)

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