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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 February 2024) | Viewed by 14240

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Special Issue Editors

Prof. Dr. Ning Li

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Guest Editor

College of Computer and Information Engineering, Henan University, Kaifeng 475004, China
Interests: new radar systems; radar imaging; radar jamming/anti-jamming; radar polarimetric theory; radar target detection
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Koo Voon Chet

E-Mail Website
Guest Editor

Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Malacca 75450, Malaysia
Interests: synthetic aperture radar; radar signal processing; radar calibration; interferometric SAR; classification and detection

Special Issue Information

Dear Colleagues,

Radar has the characteristics of all-weather, all-day, and has a certain penetration ability. It has been widely used for weather forecasting, resource detection, environmental monitoring, etc. Today, with the rapid development of digital signal processing technology, the function, performance, and systems of radar are improving, and therefore the application requirements of radar sensors are constantly expanding, providing new opportunities and challenges for the development of microwave and wireless communication systems in the future.

Radar needs signal processing to analyze or transform the observed signal, in order to suppress undesired signals such as interference and clutter, enhance the useful signal, estimate the parameters of the interested signal, and convert the signal into a more satisfactory form. Newly developed radar systems such as cognitive radar, passive radar, distributed radar, MIMO radar, and ultra-wideband radar have been used in all walks of life. To meet the above radar signal-processing requirements, technologies such as artificial intelligence, phased array technology, digital array technology, radar networking technology, OFDM technology, and multi-sensor fusion technology have been developed and applied. As an important part of the radar system, radar signal processing has always been at the forefront of national defense technology and electronic information technology.

This Special Issue will summarize the innovative and breakthrough high-level research results in the field of radar signal processing, cover the advanced techniques in this field, and look forward to the development direction of this field in the future.

New radar systems;
Array signal processing;
Radar imaging technology;
Radar interferometry;
Radar jamming/anti-jamming technology;
Radar detection and tracking methods;
Target identification and recognition;
Waveform design and optimization;
Radar early warning detection technology;
Artificial intelligence applied in radar;
Other emerging techniques;
Radar applications.

Prof. Dr. Ning Li
Prof. Dr. Koo Voon Chet
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. Electronics 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 2400 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

radar signal processing
new radar systems
artificial intelligence

Published Papers (11 papers)

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Research

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10 pages, 1209 KiB

Open AccessCommunication

Chirp Rate Estimation of LFM Signals Based on Second-Order Synchrosqueezing Transform

by Gangyi Zhai, Jianjiang Zhou, Kanglin Yu and Jiangtao Li

Electronics 2023, 12(24), 4938; https://doi.org/10.3390/electronics12244938 - 08 Dec 2023

Viewed by 603

Abstract

For the problem of low time-frequency aggregation of the short-time Fourier transform (STFT), which causes the parameter estimation performance degradation of linear frequency modulation (LFM) signals at low signal-to-noise ratio (SNR), second-order synchrosqueezing transform (SSST) is proposed based on the square of STFT amplitude. The time-frequency resolution and energy aggregation are improved by means of squeezing and reassigning the time-frequency spectrum. Meanwhile, in order to decrease the calculation of classical parameter estimation methods, the Hough transform is used for rough estimation, and then the fractional Fourier transform (FRFT) is used for accuracy estimation based on the Renyi entropy. The simulation result shows that higher estimation accuracy is obtained at low SNR, and it has better robustness. Full article

(This article belongs to the Special Issue Advancements in Radar Signal Processing)

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16 pages, 25580 KiB

Open AccessCommunication

Moving Target Detection Algorithm for Millimeter Wave Radar Based on Keystone-2DFFT

by Wenjie Shen, Sijie Wang, Yanping Wang, Yang Li, Yun Lin, Ye Zhou and Xueyong Xu

Electronics 2023, 12(23), 4776; https://doi.org/10.3390/electronics12234776 - 25 Nov 2023

Viewed by 776

Abstract

Millimeter wave radar has the advantage of all-day and all-weather capability for detection, speed measurement. It plays an important role in urban traffic flow monitoring and traffic safety monitoring. The conventional 2-dimensional Fast Fourier Transform (2DFFT) algorithm is performed target detection in the range-Doppler domain. However, the target motion will induce the range walk phenomenon, which leads to a decrease in the target energy and the performance of the target detection and speed measurement. To solve the above problems, this paper proposes a moving vehicle detection algorithm based on Keystone-2DFFT for a traffic scene. Firstly, this paper constructs and analyzes the Frequency Modulated ContinuousWave (FMCW) moving target signal model under traffic monitoring scenario’s radar observation geometry. The traditional 2DFFT moving target detection algorithm is briefly introduced. Then, based on mentioned signal model, an improved moving vehicle detection algorithm based on Keystone-2DFFT transform is proposed. The method first input the echo, then the range walk is removed by keystone transformation. the keystone transformation is achieved via Sinc interpolation. Next is transform data into range-Doppler domain to perform detection and speed estimation. The algorithm is verified by simulation data and real data. Full article

(This article belongs to the Special Issue Advancements in Radar Signal Processing)

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14 pages, 462 KiB

Open AccessArticle

Near-Field-to-Far-Field RCS Prediction Using Only Amplitude Estimation Technique Based on State Space Method

by Jinhai Huang, Jianjiang Zhou and Yao Deng

Electronics 2023, 12(15), 3371; https://doi.org/10.3390/electronics12153371 - 07 Aug 2023

Viewed by 801

Abstract

Measuring the radar cross-section (RCS) of a far-field (FF) target in engineering can be challenging, especially when remote measurement is difficult. To overcome this challenge, an FF RCS can be predicted by near-field (NF)-extrapolated transformation. However, due to the relative error between the theoretical and measured electric field (E-field) values in a NF, the extrapolation calculation of a FF can be carried out by correcting the NF amplitude. This paper proposes the use of the state space method (SSM) to estimate the amplitude-only of NF E-fields for improving the prediction accuracy of FFs. The simulation results demonstrate that the SSM can estimate NF amplitude, which can be transformed into a FF, and which can lead to improved prediction accuracy when compared to reference-FF-calculated and to circular-NF-to-FF-transform-(CNFFFT)-calculated RCSs. Full article

(This article belongs to the Special Issue Advancements in Radar Signal Processing)

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16 pages, 5084 KiB

Open AccessCommunication

Water-Body Detection in Sentinel-1 SAR Images with DK-CO Network

by Youping Xie, Haibo Zeng, Kaijun Yang, Qiming Yuan and Chao Yang

Electronics 2023, 12(14), 3163; https://doi.org/10.3390/electronics12143163 - 21 Jul 2023

Cited by 3 | Viewed by 1263

Abstract

Synthetic Aperture Radar (SAR) is an active microwave sensor with all-day/night and all-weather detection capability, which is crucial for detecting surface water resources. Surface water-body such as rivers, lakes, reservoirs, and ponds usually appear as dark areas in SAR images. Accurate and automated extraction of these water bodies can provide valuable data for the management and strategic planning of surface water resources and effectively help prevent and control drought and flood disasters. However, most deep learning-based methods rely on manually labeled samples for model training and testing, which is inefficient and may introduce errors. To address this problem, this paper proposes a novel water-body detection method that combines optimization algorithms and deep learning techniques to automate water-body label extraction and improve the accuracy of water-body detection. First, this paper uses a swarm intelligence optimization algorithm, Dung Beetle Optimizer (DBO), to optimize the initial cluster center of the K-means clustering algorithm, which is called the DBO-K-means (DK) method. The DK method divides the training images into four categories and extracts the water bodies in them to generate the water-body labels required for deep learning model training and testing, and the whole process does not require human intervention. Then, the labels generated by DK and training data set images are fed into the Classifier–Optimizer (CO) for training. The classifier performs a dense classification task at the pixel level, resulting in an initial result image with blurred boundaries of the water body. Then, the optimizer takes this preliminary result image and the original SAR image as input, performs fine-grained optimization on the preliminary result, and finally generates a result image with a clear water-body boundary. Finally, we evaluated the accuracy of water-body detection using multiple performance indicators including ACC, precision, F1-Score, recall, and Kappa coefficient. The results show that the values of all indicators exceed 93%, which demonstrates the high accuracy and reliability of our proposed water-body detection method. Overall, this paper presents a novel DK-based approach that improves the automation and accuracy of deep learning methods for detecting water bodies in SAR images by enabling automatic sample extraction and optimization of deep learning models. Full article

(This article belongs to the Special Issue Advancements in Radar Signal Processing)

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17 pages, 608 KiB

Open AccessArticle

Near-to-Far Field RCS Calculation Using Correction Optimization Technique

by Jinhai Huang, Jianjiang Zhou and Yao Deng

Electronics 2023, 12(12), 2711; https://doi.org/10.3390/electronics12122711 - 17 Jun 2023

Cited by 2 | Viewed by 873

Abstract

Radar cross section (RCS) is a scattering measure of an object that scatters to the radar. However, existing methods for near-field (NF) measurement and data processing rarely extract amplitude characteristics, and there is a lack of effective verification of far-field (FF) data in the process of NF to FF transformation, which leads to inaccuracies in FF prediction accuracy. In this paper, we propose a method to establish the relationship between the NF and FF RCS using the state space method (SSM), which is based on accurate estimation of the NF amplitude in NF measurement, and then deriving the FF RCS from the NF scattering signal convolved with a near-to-far kernel. The proposed solution to address the uncertainty issue in reference FF data involves using the geometric theory of diffraction (GTD) scattering center model as the reference FF data and establishing a linear equation with the derived FF model. The negative gradient search (NGS) system identification concept is used to optimize the FF model in order to reduce the discrepancy between the reference and derived values. Finally, the corrected RCS error is provided as additional proof of the effectiveness of these techniques in enhancing near-to-far transformation accuracy by examining the outcomes of three experiments. Full article

(This article belongs to the Special Issue Advancements in Radar Signal Processing)

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20 pages, 12001 KiB

Open AccessArticle

An Ice-Penetrating Signal Denoising Method Based on WOA-VMD-BD

by Danping Lu, Shaoxiang Shen, Yuxi Li, Bo Zhao, Xiaojun Liu and Guangyou Fang

Electronics 2023, 12(7), 1658; https://doi.org/10.3390/electronics12071658 - 31 Mar 2023

Cited by 1 | Viewed by 1056

Abstract

Chang’E-7 will be launched around 2026 to explore resources at the lunar south pole. Glaciers are suitable scenes on the earth for lunar penetrating radar verification. In the verification experiment, ice-penetrating signals are inevitably polluted by noise, affecting the accuracy and reliability of glacier detection. This paper proposes a denoising method for ice-penetrating signals based on the combination of whale optimization algorithm (WOA), variational mode decomposition (VMD), and the improved Bhattacharyya distance (BD). Firstly, a fitness function for WOA is established based on permutation entropy (PE), and the number of decomposition modes K and the quadratic penalty factor

α

in the VMD are optimized using WOA. Then, VMD is performed on the signal to obtain multiple intrinsic mode functions (IMFs). Finally, according to the BD, the relevant IMFs are selected for signal reconstruction and denoising. The simulation results indicate the strengths of this method in enhancing the signal-to-noise ratio (SNR), and its performance is better than empirical mode decomposition (EMD). Experiments on the detected signals of the Mengke Glacier No. 29 indicate that the WOA-VMD-BD method can efficiently eliminate noise from the data and procure well-defined layered profiles of the glacier. The research in this paper helps observe the layered details of the lunar regolith profile and interpret the data in subsequent space exploration missions. Full article

(This article belongs to the Special Issue Advancements in Radar Signal Processing)

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20 pages, 2884 KiB

Open AccessArticle

Range Deception Jamming Performance Evaluation for Moving Targets in a Ground-Based Radar Network

by Qing Ling, Penghui Huang, Donghong Wang, Huajian Xu, Lingyu Wang, Xingzhao Liu, Guisheng Liao and Yongyan Sun

Electronics 2023, 12(7), 1614; https://doi.org/10.3390/electronics12071614 - 29 Mar 2023

Cited by 1 | Viewed by 1623

Abstract

With the rapid development of electronic information technology, the forms and technologies of electronic warfare have become more complicated, and electronic countermeasures (ECMs) and electronic counter-countermeasures (ECCMs) have become fierce in recent years. Networked radars have become an important means of ECMs due to their “four anti-resistance performance” against electronic jamming, anti-stealth, anti-radiation missiles, and low-altitude penetration. Based on this, this paper evaluates the performance of range deception jamming on an air-based jammer in a ground-based radar network. In this paper, the ground-based radar coordinate system conversion relationship is first established. Then, the statistical variance data fusion criterion for the radar network is constructed. Hence, based on the data fusion criterion, the jamming range delay boundary and the radar position information are recorded. Finally, the jamming performance evaluation can be achieved by analyzing the relationship between the jamming range delay and the radar position. The results of the simulated experiments reveal that when the jamming range delay is sufficiently small, the radar network system can be interfered with successfully by the range false target. Full article

(This article belongs to the Special Issue Advancements in Radar Signal Processing)

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12 pages, 3504 KiB

Open AccessCommunication

Robust Phase Bias Estimation Method for Azimuth Multi-Channel HRWS SAR System Based on Maximum Modified Kurtosis

by Xingbo Pan, Hanqing Zhang and Gaofeng Shu

Electronics 2022, 11(22), 3821; https://doi.org/10.3390/electronics11223821 - 20 Nov 2022

Cited by 2 | Viewed by 1175

Abstract

The azimuth multi-channel synthetic aperture radar (MC-SAR) systems can simultaneously realize high-resolution and wide-swath (HRWS) earth observations. However, channel phase bias inevitably exists in the practical work of the azimuth MC-SAR system, which is the main factor for the “virtual target” in SAR images. To accurately estimate the phase bias, a channel phase bias estimation approach based on modified kurtosis maximization (MMK) is proposed in this paper. By analyzing the echo characteristics of multi-channel SAR, the proposed approach constructs the objective optimization function of MMK of the reconstructed Doppler spectrum (RDS), and the channel phase bias can be accurately estimated. Simulation experiments and real raw data processing verify the effectiveness and robustness of the proposed approach, which is not limited by the scene and has a good estimation performance at a low signal-to-noise ratio (SNR). Full article

(This article belongs to the Special Issue Advancements in Radar Signal Processing)

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17 pages, 6089 KiB

Open AccessArticle

HA-Unet: A Modified Unet Based on Hybrid Attention for Urban Water Extraction in SAR Images

by Huina Song, Han Wu, Jianhua Huang, Hua Zhong, Meilin He, Mingkun Su, Gaohang Yu, Mengyuan Wang and Jianwu Zhang

Electronics 2022, 11(22), 3787; https://doi.org/10.3390/electronics11223787 - 17 Nov 2022

Cited by 5 | Viewed by 1535

Abstract

Urban water plays a significant role in the urban ecosystem, but urban water extraction is still a challenging task in automatic interpretation of synthetic aperture radar (SAR) images. The influence of radar shadows and strong scatters in urban areas may lead to misclassification in urban water extraction. Nevertheless, the local features captured by convolutional layers in Convolutional Neural Networks (CNNs) are generally redundant and cannot make effective use of global information to guide the prediction of water pixels. To effectively emphasize the identifiable water characteristics and fully exploit the global information of SAR images, a modified Unet based on hybrid attention mechanism is proposed to improve the performance of urban water extraction in this paper. Considering the feature extraction ability and the global modeling capability in SAR image segmentation, the Channel and Spatial Attention Module (CSAM) and the Multi-head Self-Attention Block (MSAB) are both introduced into the proposed Hybrid Attention Unet (HA-Unet). In this work, Resnet50 is adopted as the backbone of HA-Unet to extract multi-level features of SAR images. During the feature extraction process, CSAM based on local attention is adopted to enhance the meaningful water features and ignore unnecessary features adaptively in feature maps of two shallow layers. In the last two layers of the backbone, MSAB is introduced to capture the global information of SAR images to generate global attention. In addition, two global attention maps generated by MSAB are aggregated together to reconstruct the spatial feature relationship of SAR images from high-resolution feature maps. The experimental results on Sentinel-1A SAR images show that the proposed urban water extraction method has a strong ability to extract water bodies in the complex urban areas. The ablation experiment and visualization results vividly indicate that both CSAM and MSAB contribute significantly to extracting urban water accurately and effectively. Full article

(This article belongs to the Special Issue Advancements in Radar Signal Processing)

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15 pages, 6561 KiB

Open AccessArticle

Automatic Detection of Diseases in Tunnel Linings Based on a Convolution Neural Network and a Support Vector Machine

by Lili Hou, Qian Zhang and Ruixue Zhang

Electronics 2022, 11(20), 3290; https://doi.org/10.3390/electronics11203290 - 12 Oct 2022

Cited by 2 | Viewed by 1155

Abstract

The complexity of diseases in tunnel linings and the interference of clutter and the strong reflection of rebar in ground-penetrating radar (GPR) data are the important factors that lead to the low accuracy and poor automation of disease detection. As consequence, this paper carries out an automatic detection method for hidden lining diseases. Firstly, in order to suppress the interference of strong clutter, the state equation and measurement equation of GPR data are established, and the recursive formula of clutter suppression is deduced. Secondly, combined with a convolution neural network, the network which can suppress the strong reflection of rebar is built. Finally, the multi-dimensional characteristics of disease in the time domain, frequency domain, and time-frequency domain are extracted, and then the support vector machine (SVM) data set is established and the automatic detection method for diseases is formed. The proposed method can avoid the low efficiency of manual interpretation and the over-dependence of detection accuracy of relying upon the experience level of technicians. Full article

(This article belongs to the Special Issue Advancements in Radar Signal Processing)

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Review

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17 pages, 15792 KiB

Open AccessReview

Non-Contact Human Vital Signs Extraction Algorithms Using IR-UWB Radar: A Review

by Zhihuan Liang, Mingyao Xiong, Yanghao Jin, Jianlai Chen, Dangjun Zhao, Degui Yang, Buge Liang and Jinjun Mo

Electronics 2023, 12(6), 1301; https://doi.org/10.3390/electronics12061301 - 08 Mar 2023

Cited by 6 | Viewed by 2142

Abstract

The knowledge of heart and respiratory rates (HRs and RRs) is essential in assessing human body static. This has been associated with many applications, such as survivor rescue in ruins, lie detection, and human emotion detection. Thus, the vital signal extraction from radar echoes after pre-treatments, which have been applied using various methods by many researchers, has exceedingly become a necessary part of its further usage. In this review, we describe the variety of techniques used for vital signal extraction and verify their accuracy and efficiency. Emerging approaches such as wavelet analysis and mode decomposition offer great opportunities to measure vital signals. These developments would promote advancements in industries such as medical and social security by replacing the current electrocardiograms (ECGs), emotion detection for survivor status assessment, polygraphs, etc. Full article

(This article belongs to the Special Issue Advancements in Radar Signal Processing)

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