Moving Target Detection in Multi-Static GNSS-Based Passive Radar Based on Multi-Bernoulli Filter
Over the past few years, the global navigation satellite system (GNSS)-based passive radar (GBPR) has attracted more and more attention and has developed very quickly. However, the low power level of GNSS signal limits its application. To enhance the ability of moving target detection, a multi-stati...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/21/3495 |
| _version_ | 1797549942620815360 |
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| author | HongCheng Zeng Jie Chen PengBo Wang Wei Liu XinKai Zhou Wei Yang |
| author_facet | HongCheng Zeng Jie Chen PengBo Wang Wei Liu XinKai Zhou Wei Yang |
| author_sort | HongCheng Zeng |
| collection | DOAJ |
| description | Over the past few years, the global navigation satellite system (GNSS)-based passive radar (GBPR) has attracted more and more attention and has developed very quickly. However, the low power level of GNSS signal limits its application. To enhance the ability of moving target detection, a multi-static GBPR (MsGBPR) system is considered in this paper, and a modified iterated-corrector multi-Bernoulli (ICMB) filter is also proposed. The likelihood ratio model of the MsGBPR with range-Doppler map is first presented. Then, a signal-to-noise ratio (SNR) online estimation method is proposed, which can estimate the fluctuating and unknown map SNR effectively. After that, a modified ICMB filter and its sequential Monte Carlo (SMC) implementation are proposed, which can update all measurements from multi-transmitters in the optimum order (ascending order). Moreover, based on the proposed method, a moving target detecting framework using MsGBPR data is also presented. Finally, performance of the proposed method is demonstrated by numerical simulations and preliminary experimental results, and it is shown that the position and velocity of the moving target can be estimated accurately. |
| first_indexed | 2024-03-10T15:21:53Z |
| format | Article |
| id | doaj.art-98cb705dadc64a6585fe2ab87603e181 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:21:53Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-98cb705dadc64a6585fe2ab87603e1812023-11-20T18:24:29ZengMDPI AGRemote Sensing2072-42922020-10-011221349510.3390/rs12213495Moving Target Detection in Multi-Static GNSS-Based Passive Radar Based on Multi-Bernoulli FilterHongCheng Zeng0Jie Chen1PengBo Wang2Wei Liu3XinKai Zhou4Wei Yang5School of Electronics and Information Engineering, Beihang University, Beijing 100191, ChinaSchool of Electronics and Information Engineering, Beihang University, Beijing 100191, ChinaSchool of Electronics and Information Engineering, Beihang University, Beijing 100191, ChinaDepartment of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UKSchool of Electronics and Information Engineering, Beihang University, Beijing 100191, ChinaSchool of Electronics and Information Engineering, Beihang University, Beijing 100191, ChinaOver the past few years, the global navigation satellite system (GNSS)-based passive radar (GBPR) has attracted more and more attention and has developed very quickly. However, the low power level of GNSS signal limits its application. To enhance the ability of moving target detection, a multi-static GBPR (MsGBPR) system is considered in this paper, and a modified iterated-corrector multi-Bernoulli (ICMB) filter is also proposed. The likelihood ratio model of the MsGBPR with range-Doppler map is first presented. Then, a signal-to-noise ratio (SNR) online estimation method is proposed, which can estimate the fluctuating and unknown map SNR effectively. After that, a modified ICMB filter and its sequential Monte Carlo (SMC) implementation are proposed, which can update all measurements from multi-transmitters in the optimum order (ascending order). Moreover, based on the proposed method, a moving target detecting framework using MsGBPR data is also presented. Finally, performance of the proposed method is demonstrated by numerical simulations and preliminary experimental results, and it is shown that the position and velocity of the moving target can be estimated accurately.https://www.mdpi.com/2072-4292/12/21/3495multi-static GBPRmoving target detectioniterated-corrector multi-BernoulliSNR online estimation |
| spellingShingle | HongCheng Zeng Jie Chen PengBo Wang Wei Liu XinKai Zhou Wei Yang Moving Target Detection in Multi-Static GNSS-Based Passive Radar Based on Multi-Bernoulli Filter Remote Sensing multi-static GBPR moving target detection iterated-corrector multi-Bernoulli SNR online estimation |
| title | Moving Target Detection in Multi-Static GNSS-Based Passive Radar Based on Multi-Bernoulli Filter |
| title_full | Moving Target Detection in Multi-Static GNSS-Based Passive Radar Based on Multi-Bernoulli Filter |
| title_fullStr | Moving Target Detection in Multi-Static GNSS-Based Passive Radar Based on Multi-Bernoulli Filter |
| title_full_unstemmed | Moving Target Detection in Multi-Static GNSS-Based Passive Radar Based on Multi-Bernoulli Filter |
| title_short | Moving Target Detection in Multi-Static GNSS-Based Passive Radar Based on Multi-Bernoulli Filter |
| title_sort | moving target detection in multi static gnss based passive radar based on multi bernoulli filter |
| topic | multi-static GBPR moving target detection iterated-corrector multi-Bernoulli SNR online estimation |
| url | https://www.mdpi.com/2072-4292/12/21/3495 |
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