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Signal Processing in Radar Systems
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Signal Processing in Radar Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 9527

Special Issue Editors

Dr. Lei Zhang

E-Mail Website
Guest Editor
School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510275, China
Interests: radar signal processing; distributed radar system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yulai Cong

E-Mail Website
Guest Editor
ECE, Duke University, Durham, NC 27708, USA
Interests: machine learning; radar signal processing

Special Issue Information

Dear Colleagues,

Working with electromagnetic waves, radar is a special kind of sensor with versatile capabilities and great potential. It has been widely used in, e.g., air and terrestrial traffic control, radar astronomy, air-defense systems, aircraft anti-collision systems, outer space surveillance and rendezvous systems, self-driving cars, etc. For more powerful and intelligent radar systems, advanced radar signal processing, target recognition, and machine learning techniques are of vital importance. This Special Issue is addressed to all types of novel techniques developed for Radar Systems. Potential topics include, but are not limited to:

  • Radar signal processing;
  • Automatic target recognition;
  • Object detection and tracking;
  • Generative adversarial learning;
  • Signal processing over graphs;
  • Signal processing for big data;
  • Signal processing theory and methods;
  • Machine learning; 
  • Optimization.

Dr. Lei Zhang
Prof. Dr. Yulai Cong
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. Sensors 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 2600 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.

Published Papers (8 papers)

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Research

13 pages, 2934 KiB  
Article
Transient Interference Excision and Spectrum Reconstruction with Partial Samples Using Modified Alternating Direction Method of Multipliers-Net for the Over-the-Horizon Radar
by Zhang Man, Quan Huang and Jia Duan
Sensors 2024, 24(9), 2770; https://doi.org/10.3390/s24092770 - 26 Apr 2024
Viewed by 237
Abstract
Transient interference often submerges the actual targets when employing over-the-horizon radar (OTHR) to detect targets. In addition, modern OTHR needs to carry out multi-target detection from sea to air, resulting in the sparse sampling of echo data. The sparse OTHR signal will raise [...] Read more.
Transient interference often submerges the actual targets when employing over-the-horizon radar (OTHR) to detect targets. In addition, modern OTHR needs to carry out multi-target detection from sea to air, resulting in the sparse sampling of echo data. The sparse OTHR signal will raise serious grating lobes using conventional methods and thus degrade target detection performance. This article proposes a modified Alternating Direction Method of Multipliers (ADMM)-Net to reconstruct the target and clutter spectrum of sparse OTHR signals so that target detection can be performed normally. Firstly, transient interferences are identified based on the sparse basis representation and then excised. Therefore, the processed signal can be seen as a sparse OTHR signal. By solving the Doppler sparsity-constrained optimization with the trained network, the complete Doppler spectrum is reconstructed effectively for target detection. Compared with traditional sparse solution methods, the presented approach can balance the efficiency and accuracy of OTHR signal spectrum reconstruction. Both simulation and real-measured OTHR data proved the proposed approach’s performance. Full article
(This article belongs to the Special Issue Signal Processing in Radar Systems)
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16 pages, 7278 KiB  
Article
Migration through Resolution Cell Correction and Sparse Aperture ISAR Imaging for Maneuvering Target Based on Whale Optimization Algorithm—Fast Iterative Shrinkage Thresholding Algorithm
by Xinrong Guo, Fengkai Liu and Darong Huang
Sensors 2024, 24(7), 2148; https://doi.org/10.3390/s24072148 - 27 Mar 2024
Viewed by 416
Abstract
Targets faced by inverse synthetic aperture radar (ISAR) are often non-cooperative, with target maneuvering being the main manifestation of this non-cooperation. Maneuvers cause ISAR imaging results to be severely defocused, which can create huge difficulties in target identification. In addition, as the ISAR [...] Read more.
Targets faced by inverse synthetic aperture radar (ISAR) are often non-cooperative, with target maneuvering being the main manifestation of this non-cooperation. Maneuvers cause ISAR imaging results to be severely defocused, which can create huge difficulties in target identification. In addition, as the ISAR bandwidth continues to increase, the impact of migration through resolution cells (MTRC) on imaging results becomes more significant. Target non-cooperation may also result in sparse aperture, leading to the failure of traditional ISAR imaging algorithms. Therefore, this paper proposes an algorithm to realize MTRC correction and sparse aperture ISAR imaging for maneuvering targets simultaneously named whale optimization algorithm–fast iterative shrinkage thresholding algorithm (WOA-FISTA). In this algorithm, FISTA is used to perform MTRC correction and sparse aperture ISAR imaging efficiently and WOA is adopted to estimate the rotational parameter to eliminate the effects of maneuvering on imaging results. Experimental results based on simulation and measured datasets prove that the proposed algorithm implements sparse aperture ISAR imaging and MTRC correction for maneuvering targets simultaneously. The proposed algorithm achieves better results than traditional algorithms under different signal-to-noise ratio conditions. Full article
(This article belongs to the Special Issue Signal Processing in Radar Systems)
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17 pages, 3718 KiB  
Article
Enhancing Radar Echo Extrapolation by ConvLSTM2D for Precipitation Nowcasting
by Farah Naz, Lei She, Muhammad Sinan and Jie Shao
Sensors 2024, 24(2), 459; https://doi.org/10.3390/s24020459 - 11 Jan 2024
Viewed by 684
Abstract
Precipitation nowcasting in real-time is a challenging task that demands accurate and current data from multiple sources. Despite various approaches proposed by researchers to address this challenge, models such as the interaction-based dual attention LSTM (IDA-LSTM) face limitations, particularly in radar echo extrapolation. [...] Read more.
Precipitation nowcasting in real-time is a challenging task that demands accurate and current data from multiple sources. Despite various approaches proposed by researchers to address this challenge, models such as the interaction-based dual attention LSTM (IDA-LSTM) face limitations, particularly in radar echo extrapolation. These limitations include higher computational costs and resource requirements. Moreover, the fixed kernel size across layers in these models restricts their ability to extract global features, focusing more on local representations. To address these issues, this study introduces an enhanced convolutional long short-term 2D (ConvLSTM2D) based architecture for precipitation nowcasting. The proposed approach includes time-distributed layers that enable parallel Conv2D operations on each image input, enabling effective analysis of spatial patterns. Following this, ConvLSTM2D is applied to capture spatiotemporal features, which improves the model’s forecasting skills and computational efficacy. The performance evaluation employs a real-world weather dataset benchmarked against established techniques, with metrics including the Heidke skill score (HSS), critical success index (CSI), mean absolute error (MAE), and structural similarity index (SSIM). ConvLSTM2D demonstrates superior performance, achieving an HSS of 0.5493, a CSI of 0.5035, and an SSIM of 0.3847. Notably, a lower MAE of 11.16 further indicates the model’s precision in predicting precipitation. Full article
(This article belongs to the Special Issue Signal Processing in Radar Systems)
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17 pages, 21450 KiB  
Article
Agricultural Application Prospect of Fully Polarimetric and Quantification S-Band SAR Subsystem in Chinese High-Resolution Aerial Remote Sensing System
by Yabo Liu, Luhao Wang, Shuang Zhu, Xiaojie Zhou, Jia Liu and Binghong Xie
Sensors 2024, 24(1), 236; https://doi.org/10.3390/s24010236 - 31 Dec 2023
Cited by 1 | Viewed by 675
Abstract
The synthetic aperture radar (SAR) is a type of active radar that can obtain polarization scattering information of ground objects, which is an important supplement to optical remote sensing. This paper designs a high-precision quantitative SAR system that combines radiation and polarization calibration [...] Read more.
The synthetic aperture radar (SAR) is a type of active radar that can obtain polarization scattering information of ground objects, which is an important supplement to optical remote sensing. This paper designs a high-precision quantitative SAR system that combines radiation and polarization calibration processing to achieve a subtle perception of the changes in soil moisture and straw coverage. In Yushu, Jilin, we conducted the first S-band agricultural remote sensing application experiment. The backscattering coefficient was measured under different water content and straw coverage conditions, and the results showed that the backscattering coefficient increased by about 2 dB and 6 dB, respectively. We estimated that the soil water content increased by about 0.01 cm3/cm3, which was consistent with the theoretical analysis. The polarization scattering characteristics also showed significant differences under different straw coverage. The results indicated that S-band quantitative SAR had an excellent response ability to water content and straw coverage, which provided a technical basis for further radar agricultural applications in the future. Full article
(This article belongs to the Special Issue Signal Processing in Radar Systems)
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16 pages, 3502 KiB  
Article
Resolution Enhancement for Millimeter-Wave Radar ROI Image with Bayesian Compressive Sensing
by Pengfei Xie, Jianxin Wu, Lei Zhang, Guanyong Wang and Xue Jin
Sensors 2022, 22(15), 5757; https://doi.org/10.3390/s22155757 - 02 Aug 2022
Cited by 1 | Viewed by 1719
Abstract
For millimeter-wave (MMW) imaging security systems, the image resolution promisingly determines the performance of suspicious target detection and recognition. Conventional synthetic aperture radar (SAR) imaging algorithms only provide limited resolution in active MMW imaging, which is limited by the system. In terms of [...] Read more.
For millimeter-wave (MMW) imaging security systems, the image resolution promisingly determines the performance of suspicious target detection and recognition. Conventional synthetic aperture radar (SAR) imaging algorithms only provide limited resolution in active MMW imaging, which is limited by the system. In terms of enhancing the resolution of a region of interest (ROI) image containing suspicious targets, super-resolution (SR) imaging is adopted via Bayesian compressive sensing (BCS) implemented by fast Fourier transform (FFT). The spatial sparsity of MMW ROI images is well exploited with BCS to achieve resolution enhancement without computational cost. Both simulated and measured experiments confirm that the proposed scheme effectively improves the resolution of ROI images. Full article
(This article belongs to the Special Issue Signal Processing in Radar Systems)
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19 pages, 9136 KiB  
Article
Costas DFC-Based Random Stepped Wideband Waveform for Interference Countermeasure in SAR Imagery
by GanE Dai, Lei Zhang and Sha Huan
Sensors 2022, 22(9), 3197; https://doi.org/10.3390/s22093197 - 21 Apr 2022
Cited by 1 | Viewed by 1402
Abstract
Interference in SAR imagery will induce false targets or form a mask in specific areas to prevent accurate scene assessment. Traditional anti-jamming methods based on waveform agility require a trade-off between anti-jamming performance and imaging quality in waveform design. In this paper, we [...] Read more.
Interference in SAR imagery will induce false targets or form a mask in specific areas to prevent accurate scene assessment. Traditional anti-jamming methods based on waveform agility require a trade-off between anti-jamming performance and imaging quality in waveform design. In this paper, we proposed a SAR ECCM scheme including a Costas DFC-based random stepped wideband waveform and corresponding imaging processing method. The waveform exhibits high flexibility against forwarding interference due to the decomposition of a wideband signal into multiple pulses with different Costas discrete frequency encoding, carrier frequency and phase modulation. Furthermore, the combination of FCDC and the imaging processing successfully overcomes the Doppler sensitivity of the proposed waveform. Extensive simulations confirmed the superiority of this waveform and processing method under different interference strategies. Full article
(This article belongs to the Special Issue Signal Processing in Radar Systems)
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15 pages, 3722 KiB  
Article
Channel Phase Calibration for High-Resolution and Wide-Swath SAR Imaging with Doppler Spectrum Sharpness Optimization
by Man Zhang, Sha Huan, Zeya Zhao and Zhibin Wang
Sensors 2022, 22(5), 1781; https://doi.org/10.3390/s22051781 - 24 Feb 2022
Viewed by 1234
Abstract
Channel phase calibration is a crucial issue in high resolution and wide swath (HRWS) imagery with azimuth multi-channel synthetic aperture radar (SAR) systems. Precise phase calibration is definitely required in reconstructing the full Doppler spectrum for precise HRWS imagery without high-level ambiguities. In [...] Read more.
Channel phase calibration is a crucial issue in high resolution and wide swath (HRWS) imagery with azimuth multi-channel synthetic aperture radar (SAR) systems. Precise phase calibration is definitely required in reconstructing the full Doppler spectrum for precise HRWS imagery without high-level ambiguities. In this paper, we propose a novel calibration for HRWS SAR imagery by optimizing the reconstructed unambiguous Doppler spectrum. The sharpness of the reconstructed Doppler spectrum is applied as the metric to measure the unambiguity quality, which is maximized to retrieve the element phase error caused by channel imbalance. Real data experiments demonstrate the performance of the proposed calibration for ambiguity suppression in HRWS SAR imagery. Full article
(This article belongs to the Special Issue Signal Processing in Radar Systems)
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17 pages, 792 KiB  
Article
Hybrid Interacting Multiple Model Filtering for Improving the Reliability of Radar-Based Forward Collision Warning Systems
by Jung Min Pak
Sensors 2022, 22(3), 875; https://doi.org/10.3390/s22030875 - 24 Jan 2022
Cited by 6 | Viewed by 2243
Abstract
Automotive forward collision warning (FCW) systems based on radar sensors attracted widespread attention in recent years. To achieve a reliable FCW, it is essential to accurately estimate the position and velocity of a preceding vehicle. To this end, this study proposed a novel [...] Read more.
Automotive forward collision warning (FCW) systems based on radar sensors attracted widespread attention in recent years. To achieve a reliable FCW, it is essential to accurately estimate the position and velocity of a preceding vehicle. To this end, this study proposed a novel estimation algorithm, which is a hybrid of interacting multiple model probabilistic data association (IMM-PDA) and finite impulse response (FIR) filters. Although the IMM-PDA filter is one of the most successful algorithm for tracking a maneuvering target in clutters, it sometimes exhibits divergence owing to modeling errors. In this study, the divergent IMM-PDA filter in the novel algorithm was reset and recovered using an assisting FIR filter. Consequently, this enabled reliable estimation for FCW. The improved reliability of the proposed algorithm was demonstrated through the simulation of preceding vehicle tracking using automotive radars. Full article
(This article belongs to the Special Issue Signal Processing in Radar Systems)
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