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Target Detection, Tracking and Imaging Based on Radar
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Target Detection, Tracking and Imaging Based on Radar

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

Deadline for manuscript submissions: closed (26 June 2024) | Viewed by 14534

Special Issue Editors

Prof. Dr. Xueru Bai

E-Mail Website
Guest Editor
School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: high-resolution radar imaging; radar automatic target recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Li Wang

E-Mail Website
Guest Editor
Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi’an 710071, China
Interests: machine learning; signal processing; SAR target recognition; SAR image classification; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Tian Tian

E-Mail Website
Guest Editor
School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: radar signal processing; radar behavior perception; radar countermeasures
Dr. Weiwei Fan

E-Mail Website
Guest Editor
School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: radar signal processing; interference mitigation for SAR; radar anti-jamming

Special Issue Information

Dear Colleagues,

Radar is one of the most important sensors used to obtain all-weather and all-day information on aspects related to the land, sea, air and sky, and has been widely used in military and civilian areas. However, with the development of the radar system, the requirements of radar signal processing are becoming increasingly strict.  This Topic aims to explore the application of advanced technology in target detection, tracking and imaging based on radar, and seeks new ideas to improve the performance of radar signal processing. Articles on theoretical, application-oriented, and experimental studies with an emphasis on novelty in radar signal processing will be considered. Topics of interest include, but are not limited to, radar target detection, radar target tracking and radar imaging.

Prof. Dr. Xueru Bai
Dr. Li Wang
Dr. Tian Tian
Dr. Weiwei Fan
Guest Editors

Manuscript Submission Information

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

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

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

Keywords

  • radar signal processing
  • automatic target recognition
  • radar imaging
  • radar target detection
  • anti-jamming
  • interference mitigation

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Published Papers (9 papers)

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Research

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25 pages, 4130 KiB  
Article
Adaptive Multi-Hypothesis Marginal Bayes Filter for Tracking Multiple Targets
by Zongxiang Liu, Zikang Qiu, Zhijian Gao and Jie Zhang
Remote Sens. 2024, 16(12), 2154; https://doi.org/10.3390/rs16122154 - 13 Jun 2024
Cited by 1 | Viewed by 1011
Abstract
Tracking multiple targets in the presence of unknown number of targets, missed detection, clutter, and noise is a challenging problem. To cope with this problem, a novel approach for generating the potential birth targets was developed, a mathematical model for multiple hypotheses was [...] Read more.
Tracking multiple targets in the presence of unknown number of targets, missed detection, clutter, and noise is a challenging problem. To cope with this problem, a novel approach for generating the potential birth targets was developed, a mathematical model for multiple hypotheses was established, and an adaptive multi-hypothesis marginal Bayes filter is herein proposed in terms of the established mathematical model for multiple hypotheses and the novel birth approach. This filter delivers the existence probabilities of targets and their probability density functions. It uses multiple hypotheses to solve the data association problem to form the existence probabilities of targets and their probability density functions. To obviate the requirement for prior birth models, this filter uses the observations from two consecutive time steps to establish the birth models adaptively. Its tracking performance was tested by comparing it with other adaptive filters, showing that the proposed filter is robust, and it can obtain higher tracking accuracy than other filters. Full article
(This article belongs to the Special Issue Target Detection, Tracking and Imaging Based on Radar)
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21 pages, 1769 KiB  
Article
Adaptive Resource Scheduling Algorithm for Multi-Target ISAR Imaging in Radar Systems
by Huan Yao, Hao Lou, Dan Wang, Yijun Chen and Ying Luo
Remote Sens. 2024, 16(9), 1496; https://doi.org/10.3390/rs16091496 - 24 Apr 2024
Viewed by 1330
Abstract
Inverse synthetic-aperture radar (ISAR) can achieve precise imaging of targets, which enables precise perception of battlefield information, and it has become one of the most important tasks for radar systems. In multi-target scenarios, a resource scheduling method is required to improve the sensing [...] Read more.
Inverse synthetic-aperture radar (ISAR) can achieve precise imaging of targets, which enables precise perception of battlefield information, and it has become one of the most important tasks for radar systems. In multi-target scenarios, a resource scheduling method is required to improve the sensing ability and the overall efficiency of a radar system due to the limited resources. Considering the motion state of the target will change as the observation distance increases and image defocusing can occur due to the prolonged coherence accumulation time and significant changes in the target’s motion state, the optimal observation period should be an important consideration factor in the resource scheduling method to further improve the imaging efficiency of radar system, which has not yet been involved in existing research. In this paper, we first derive the expressions of the target’s effective rotation angle and the equivalent rotation angular velocity and then define the target’s optimal observation period. Then, for multi-target imaging scenarios, we allocate pulse resources within a given time period based on sparse-aperture ISAR imaging technology. An adaptive radar resource scheduling algorithm for multi-target ISAR imaging is proposed, which prioritizes allocating resources based on the optimal observation periods for the targets. In the algorithm, a radar resource scheduling model for multi-target ISAR imaging is established, and a feedback-based closed-loop search optimization method is proposed to solve the model. Finally, the best scheduling strategy can be obtained, which includes imaging task duration and the pulse allocation sequence for each target. Simulation results validate the effectiveness of the algorithm. Full article
(This article belongs to the Special Issue Target Detection, Tracking and Imaging Based on Radar)
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15 pages, 4228 KiB  
Article
A Coherent Integration Method for Moving Target Detection in Frequency Agile Signal-Based Passive Bistatic Radar
by Luo Zuo, Nan Li, Jie Tan, Xiangyu Peng, Yunhe Cao, Zuobang Zhou and Jiusheng Han
Remote Sens. 2024, 16(7), 1148; https://doi.org/10.3390/rs16071148 - 26 Mar 2024
Cited by 1 | Viewed by 2027
Abstract
In this paper, the possibility of improving target detection performance in passive bistatic radar by exploiting a frequency agile (FA) signal is investigated, namely frequency agile signal-based passive bistatic radar (FAPBR) coherent integration. Since the carrier frequency of each pulse signal is agile, [...] Read more.
In this paper, the possibility of improving target detection performance in passive bistatic radar by exploiting a frequency agile (FA) signal is investigated, namely frequency agile signal-based passive bistatic radar (FAPBR) coherent integration. Since the carrier frequency of each pulse signal is agile, FAPBR coherent integration suffers from the problems of random range and Doppler phase fluctuations. To tackle these challenges, a novel FA signal coherent integration target detection scheme for PBR is proposed. In particular, the phase quadratic difference principle is presented for eliminating Doppler phase hopping. Then, frequency rearrangement is adopted to compensate for random range phase fluctuation while obtaining the high-range-resolution profiles (HRRPs) of the detecting target. Further, we innovatively present a sliding-range ambiguity decoupling (S-RAD) method to remove the range ambiguity effect in the case of the high pulse repetition frequency (HPRF). Compared with the existing methods, the proposed method can effectively mitigate Doppler phase hopping without requiring prior target velocity information, offering improved coherent integration performance in frequency agile signals with reduced computational complexity. Moreover, it successfully corrects the range ambiguity issue caused by HPRF. Finally, a series of simulation results are presented to demonstrate the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Target Detection, Tracking and Imaging Based on Radar)
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25 pages, 2541 KiB  
Article
TR-RAGCN-AFF-RESS: A Method for Radar Emitter Signal Sorting
by Zhizhong Zhang, Xiaoran Shi, Xinyi Guo and Feng Zhou
Remote Sens. 2024, 16(7), 1121; https://doi.org/10.3390/rs16071121 - 22 Mar 2024
Cited by 2 | Viewed by 1420
Abstract
Radar emitter signal sorting (RESS) is a crucial process in contemporary electronic battlefield situation awareness. Separating pulses belonging to the same radar emitter from interleaved radar pulse sequences with a lack of prior information, high density, strong overlap, and wide parameter distribution has [...] Read more.
Radar emitter signal sorting (RESS) is a crucial process in contemporary electronic battlefield situation awareness. Separating pulses belonging to the same radar emitter from interleaved radar pulse sequences with a lack of prior information, high density, strong overlap, and wide parameter distribution has attracted increasing attention. In order to improve the accuracy of RESS under scenarios with limited labeled samples, this paper proposes an RESS model called TR-RAGCN-AFF-RESS. This model transforms the RESS problem into a pulse-by-pulse classification task. Firstly, a novel weighted adjacency matrix construction method was proposed to characterize the structural relationships between pulse attribute parameters more accurately. Building upon this foundation, two networks were developed: a Transformer(TR)-based interleaved pulse sequence temporal feature extraction network and a residual attention graph convolutional network (RAGCN) for extracting the structural relationship features of attribute parameters. Finally, the attention feature fusion (AFF) algorithm was introduced to fully integrate the temporal features and attribute parameter structure relationship features, enhancing the richness of feature representation for the original pulses and achieving more accurate sorting results. Compared to existing deep learning-based RESS algorithms, this method does not require many labeled samples for training, making it better suited for scenarios with limited labeled sample availability. Experimental results and analysis confirm that even with only 10% of the training samples, this method achieves a sorting accuracy exceeding 93.91%, demonstrating high robustness against measurement errors, lost pulses, and spurious pulses in non-ideal conditions. Full article
(This article belongs to the Special Issue Target Detection, Tracking and Imaging Based on Radar)
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24 pages, 9096 KiB  
Article
Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
by Yu Li, Xiaoran Shi, Xiaoning Wang, Yongqiang Lu, Peipei Cheng and Feng Zhou
Remote Sens. 2024, 16(6), 1008; https://doi.org/10.3390/rs16061008 - 13 Mar 2024
Cited by 2 | Viewed by 1750
Abstract
In complex electromagnetic environments, satellite telemetry, tracking, and command (TT&C) signals often become submerged in background noise. Traditional TT&C signal detection algorithms suffer a significant performance degradation or can even be difficult to execute when phase information is absent. Currently, deep-learning-based detection algorithms [...] Read more.
In complex electromagnetic environments, satellite telemetry, tracking, and command (TT&C) signals often become submerged in background noise. Traditional TT&C signal detection algorithms suffer a significant performance degradation or can even be difficult to execute when phase information is absent. Currently, deep-learning-based detection algorithms often rely on expert-experience-driven post-processing steps, failing to achieve end-to-end signal detection. To address the aforementioned limitations of existing algorithms, we propose an intelligent satellite TT&C signal detection method based on triplet attention and Transformer (TATR). TATR introduces the residual triplet attention (ResTA) backbone network, which effectively combines spectral feature channels, frequency, and amplitude dimensions almost without introducing additional parameters. In signal detection, TATR employs a multi-head self-attention mechanism to effectively address the long-range dependency issue in spectral information. Moreover, the prediction-box-matching module based on the Hungarian algorithm eliminates the need for non-maximum suppression (NMS) post-processing steps, transforming the signal detection problem into a set prediction problem and enabling parallel output of the detection results. TATR combines the global attention capability of ResTA with the local self-attention capability of Transformer. Experimental results demonstrate that utilizing only the signal spectrum amplitude information, TATR achieves accurate detection of weak TT&C signals with signal-to-noise ratios (SNRs) of −15 dB and above (mAP@0.5 > 90%), with parameter estimation errors below 3%, which outperforms typical target detection methods. Full article
(This article belongs to the Special Issue Target Detection, Tracking and Imaging Based on Radar)
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20 pages, 11143 KiB  
Article
A New Waveform Design Method for Multi-Target Inverse Synthetic Aperture Radar Imaging Based on Orthogonal Frequency Division Multiplexing Chirp
by Xuebo Zou, Guanghu Jin, Feng He and Yongsheng Zhang
Remote Sens. 2024, 16(2), 308; https://doi.org/10.3390/rs16020308 - 11 Jan 2024
Cited by 4 | Viewed by 1458
Abstract
With the increasing use of the strategy and group target attack method in the modern battlefield, multi-target inverse synthetic aperture radar (ISAR) imaging simultaneously with high efficiency draws more and more attention, which gives a promising prospect for aerospace target detection and recognition [...] Read more.
With the increasing use of the strategy and group target attack method in the modern battlefield, multi-target inverse synthetic aperture radar (ISAR) imaging simultaneously with high efficiency draws more and more attention, which gives a promising prospect for aerospace target detection and recognition in the multi-target scenario. To overcome the shortcomings of traditional multi-target imaging with one beam at one pulse repetition time (PRT) based on phase array radar (PAR), this paper proposes a novel multi-target imaging waveform design method based on the newly full digital array radar (DAR). Firstly, we propose using radar waveform diversity with 2D orthogonality to realize multi-target ISAR imaging with high imaging quality and efficiency. Then, to meet the constant modulus requirement for maximizing the transmitting power, orthogonal frequency division multiplexing (OFDM) chirp theory is proposed to directly generate the transmit waveform instead of the traditional optimization method with the nonconvex problem for waveform design. Based on time-variant weighted and time diversity technology, a of group transmit waveforms is designed, which can form multiple beams simultaneously and make the signals arriving at different targets approximately orthogonal. Finally, simulations and experiments are carried out to demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Target Detection, Tracking and Imaging Based on Radar)
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17 pages, 2737 KiB  
Article
Rao and Wald Tests for Moving Target Detection in Forward Scatter Radar
by Zeyu Wang, Hongmeng Chen, Yachao Li and Dewu Wang
Remote Sens. 2024, 16(2), 211; https://doi.org/10.3390/rs16020211 - 5 Jan 2024
Cited by 2 | Viewed by 1456
Abstract
This paper deals with adaptive moving target detection for a forward scatter radar in complex Gaussian noise. The echoes received by the forward scatter radar include not only the noise and the possible target signals but also the direct signals. To suppress the [...] Read more.
This paper deals with adaptive moving target detection for a forward scatter radar in complex Gaussian noise. The echoes received by the forward scatter radar include not only the noise and the possible target signals but also the direct signals. To suppress the direct signals and detect the target signals, Rao and Wald tests are derived in two cases: the secondary data which contain no target signal are available or not available. Different from monostatic radar, it is proved that the derived Rao and Wald detectors for the forward scatter radar have the same test statistics as the generalized likelihood ratio test-based detector in the complex Gaussian noise both when the secondary data are available or not available. The numerical evaluation further demonstrates the equivalence and the effectiveness of the proposed detectors. Full article
(This article belongs to the Special Issue Target Detection, Tracking and Imaging Based on Radar)
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21 pages, 3036 KiB  
Article
Coherent Accumulation for Measuring Maneuvering Weak Targets Based on Stepped Dechirp Generalized Radon–Fourier Transform
by Yuxian Sun, Shaoqiang Chang, Bowen Cai, Dewu Wang and Quanhua Liu
Remote Sens. 2023, 15(21), 5161; https://doi.org/10.3390/rs15215161 - 29 Oct 2023
Viewed by 1544
Abstract
The problem of accurately measuring the motion parameters of low radar cross-section (RCS) maneuvering targets has long been a hurdle in the radar technology landscape. Small targets, due to their elusive characteristics, are particularly difficult to detect with conventional radar systems. In this [...] Read more.
The problem of accurately measuring the motion parameters of low radar cross-section (RCS) maneuvering targets has long been a hurdle in the radar technology landscape. Small targets, due to their elusive characteristics, are particularly difficult to detect with conventional radar systems. In this paper, we investigate the capabilities of dechirp-receiving stepped-frequency radar, a modern system using a linear frequency modulation signal for downconversion. This permits the radar to function at reduced sampling rates while maintaining the transmission of large-bandwidth signals and achieving synthetic imaging. Our primary contribution is introducing the stepped dechirp generalized Radon–Fourier transform (stepped DGRFT) algorithm. This novel approach allows the radar system to perform coherent accumulation, enhancing the accuracy of motion parameter estimates for low-RCS maneuvering targets. Results from our simulations and measured data analysis validate the effectiveness of our proposed algorithm, demonstrating its superiority over other methods. Full article
(This article belongs to the Special Issue Target Detection, Tracking and Imaging Based on Radar)
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14 pages, 3301 KiB  
Technical Note
Algorithm for the Weak Target Joint Detection and Ambiguity Resolution Based on Ambiguity Matrix
by Yitong Mao, Chong Song and Bingnan Wang
Remote Sens. 2024, 16(9), 1597; https://doi.org/10.3390/rs16091597 - 30 Apr 2024
Cited by 2 | Viewed by 1164
Abstract
The looking-down mode of space airship bistatic radars faces complex sea–land clutter, and the mode of wide-range surveillance and the over-sight detection of the satellite platform generates a low SNR and range–Doppler ambiguity. The method traditionally used involves the transmission of multiple Pulse [...] Read more.
The looking-down mode of space airship bistatic radars faces complex sea–land clutter, and the mode of wide-range surveillance and the over-sight detection of the satellite platform generates a low SNR and range–Doppler ambiguity. The method traditionally used involves the transmission of multiple Pulse Repetition Frequencies (PRFs) and correlating them to solve the ambiguity. However, with a low SNR, the traditional disambiguation fails due to the large number of false alarms and target omissions. In order to solve this problem, a new algorithm for multi-target joint detection and range–Doppler disambiguation based on an ambiguity matrix is presented. Firstly, all possible state values corresponding to the ambiguous sequence are filled into the ambiguity matrix one by one. Secondly, the state values in the matrix cell are divided into several groups of subsequences according to the PRF. By disambiguating multiple sets of subsequences, performing subsequence fusion, and then undertaking point aggregation, the targets can be effectively detected in scenarios with a strong clutter rate, the false alarms can be suppressed, and the disambiguation of the range and Doppler is completed. The simulation shows that the proposed algorithm has the strong ability to detect targets and perform ambiguity resolution in the scenario of a multi-target and multi-false alarm. Full article
(This article belongs to the Special Issue Target Detection, Tracking and Imaging Based on Radar)
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