Refined PHD Filter for Multi-Target Tracking under Low Detection Probability
Radar target detection probability will decrease as the target echo signal-to-noise ratio (SNR) decreases, which has an adverse influence on the result of multi-target tracking. The performances of standard multi-target tracking algorithms degrade significantly under low detection probability in pra...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/13/2842 |
| _version_ | 1798039641257934848 |
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| author | Sen Wang Qinglong Bao Zengping Chen |
| author_facet | Sen Wang Qinglong Bao Zengping Chen |
| author_sort | Sen Wang |
| collection | DOAJ |
| description | Radar target detection probability will decrease as the target echo signal-to-noise ratio (SNR) decreases, which has an adverse influence on the result of multi-target tracking. The performances of standard multi-target tracking algorithms degrade significantly under low detection probability in practice, especially when continuous miss detection occurs. Based on sequential Monte Carlo implementation of Probability Hypothesis Density (PHD) filter, this paper proposes a heuristic method called the Refined PHD (R-PHD) filter to improve multi-target tracking performance under low detection probability. In detail, this paper defines a survival probability which is dependent on target state, and labels individual extracted targets and corresponding particles. When miss detection occurs due to low detection probability, posterior particle weights will be revised according to the prediction step. Finally, we transform the target confirmation problem into a hypothesis test problem, and utilize sequential probability ratio test to distinguish real targets and false alarms in real time. Computer simulations with respect to different detection probabilities, average numbers of false alarms and continuous miss detection durations are provided to corroborate the superiority of the proposed method, compared with standard PHD filter, Cardinalized PHD (CPHD) filter and Cardinality Balanced Multi-target Multi-Bernoulli (CBMeMBer) filter. |
| first_indexed | 2024-04-11T21:56:41Z |
| format | Article |
| id | doaj.art-560acbeb83904a79965246cfff648943 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:56:41Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-560acbeb83904a79965246cfff6489432022-12-22T04:01:05ZengMDPI AGSensors1424-82202019-06-011913284210.3390/s19132842s19132842Refined PHD Filter for Multi-Target Tracking under Low Detection ProbabilitySen Wang0Qinglong Bao1Zengping Chen2National Key Laboratory of Science and Technology on ATR, National University of Defense Technology, Changsha 410073, ChinaNational Key Laboratory of Science and Technology on ATR, National University of Defense Technology, Changsha 410073, ChinaSchool of Electronics and Communication Engineering, SUN YAT-SEN University, Guangzhou 510275, ChinaRadar target detection probability will decrease as the target echo signal-to-noise ratio (SNR) decreases, which has an adverse influence on the result of multi-target tracking. The performances of standard multi-target tracking algorithms degrade significantly under low detection probability in practice, especially when continuous miss detection occurs. Based on sequential Monte Carlo implementation of Probability Hypothesis Density (PHD) filter, this paper proposes a heuristic method called the Refined PHD (R-PHD) filter to improve multi-target tracking performance under low detection probability. In detail, this paper defines a survival probability which is dependent on target state, and labels individual extracted targets and corresponding particles. When miss detection occurs due to low detection probability, posterior particle weights will be revised according to the prediction step. Finally, we transform the target confirmation problem into a hypothesis test problem, and utilize sequential probability ratio test to distinguish real targets and false alarms in real time. Computer simulations with respect to different detection probabilities, average numbers of false alarms and continuous miss detection durations are provided to corroborate the superiority of the proposed method, compared with standard PHD filter, Cardinalized PHD (CPHD) filter and Cardinality Balanced Multi-target Multi-Bernoulli (CBMeMBer) filter.https://www.mdpi.com/1424-8220/19/13/2842refined PHD filterlow detection probabilitycontinuous miss detectionradar multi-target trackingsurvival probabilitytarget labelsposterior weight revisionsequential probability ratio testhypothesis test |
| spellingShingle | Sen Wang Qinglong Bao Zengping Chen Refined PHD Filter for Multi-Target Tracking under Low Detection Probability Sensors refined PHD filter low detection probability continuous miss detection radar multi-target tracking survival probability target labels posterior weight revision sequential probability ratio test hypothesis test |
| title | Refined PHD Filter for Multi-Target Tracking under Low Detection Probability |
| title_full | Refined PHD Filter for Multi-Target Tracking under Low Detection Probability |
| title_fullStr | Refined PHD Filter for Multi-Target Tracking under Low Detection Probability |
| title_full_unstemmed | Refined PHD Filter for Multi-Target Tracking under Low Detection Probability |
| title_short | Refined PHD Filter for Multi-Target Tracking under Low Detection Probability |
| title_sort | refined phd filter for multi target tracking under low detection probability |
| topic | refined PHD filter low detection probability continuous miss detection radar multi-target tracking survival probability target labels posterior weight revision sequential probability ratio test hypothesis test |
| url | https://www.mdpi.com/1424-8220/19/13/2842 |
| work_keys_str_mv | AT senwang refinedphdfilterformultitargettrackingunderlowdetectionprobability AT qinglongbao refinedphdfilterformultitargettrackingunderlowdetectionprobability AT zengpingchen refinedphdfilterformultitargettrackingunderlowdetectionprobability |