Hybrid Particle Filter Based Dynamic Compressed Sensing for Signal-Level Multitarget Tracking
We propose a novel algorithm, state propagation based dynamic compressed sensing (SP-DCS), that uses a target dynamic model in dynamic compressed sensing (DCS) to track a fixed number of targets. To track a time-varying number of targets using raw measurements from a Doppler radar, we also propose a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8962055/ |
| _version_ | 1818379339491704832 |
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| author | Jing Liu Xiaoyu Jiang Xiaoqing Tian Mahendra Mallick Kaiyu Huang Chaoqun Ma |
| author_facet | Jing Liu Xiaoyu Jiang Xiaoqing Tian Mahendra Mallick Kaiyu Huang Chaoqun Ma |
| author_sort | Jing Liu |
| collection | DOAJ |
| description | We propose a novel algorithm, state propagation based dynamic compressed sensing (SP-DCS), that uses a target dynamic model in dynamic compressed sensing (DCS) to track a fixed number of targets. To track a time-varying number of targets using raw measurements from a Doppler radar, we also propose a novel hybrid particle filter based dynamic compressed sensing (HPF-DCS) algorithm. We calculate the support set in a Bayesian framework and a particle filter approximates the posterior probability mass function (pmf) of the support set. HPF-DCS is a combination of random and deterministic sampling. In random sampling, a number of predicted existing sub-particles are sampled from the prior pmf of the existing support set to handle the scenario when targets disappear randomly at a scan time. In deterministic sampling, the new support set corresponding to newly appearing targets is calculated by solving a sparsity promoting optimization problem. Our simulation results show that the proposed algorithm can track a time-varying number of targets successfully. It also outperforms the sequential Monte Carlo based probability hypothesis density (SMC-PHD) filter, as well as the multi-mode, multi-target track before detect (MM-MT-TBD) filter. |
| first_indexed | 2024-12-14T02:01:13Z |
| format | Article |
| id | doaj.art-f0d1d93df66a44ad8865019b35f541bf |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T02:01:13Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f0d1d93df66a44ad8865019b35f541bf2022-12-21T23:21:01ZengIEEEIEEE Access2169-35362020-01-018171341714810.1109/ACCESS.2020.29675508962055Hybrid Particle Filter Based Dynamic Compressed Sensing for Signal-Level Multitarget TrackingJing Liu0https://orcid.org/0000-0001-8341-3289Xiaoyu Jiang1Xiaoqing Tian2Mahendra Mallick3Kaiyu Huang4Chaoqun Ma5https://orcid.org/0000-0001-6338-8290Ministry of Education Key Laboratory of Intelligent and Network Security, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaDepartment of Information Communication, Army Academy of Armored Forces, Beijing, ChinaMinistry of Education Key Laboratory of Intelligent and Network Security, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaElectrical Engineering Department, University of Colorado, Boulder, CO, USAMinistry of Education Key Laboratory of Intelligent and Network Security, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaMinistry of Education Key Laboratory of Intelligent and Network Security, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaWe propose a novel algorithm, state propagation based dynamic compressed sensing (SP-DCS), that uses a target dynamic model in dynamic compressed sensing (DCS) to track a fixed number of targets. To track a time-varying number of targets using raw measurements from a Doppler radar, we also propose a novel hybrid particle filter based dynamic compressed sensing (HPF-DCS) algorithm. We calculate the support set in a Bayesian framework and a particle filter approximates the posterior probability mass function (pmf) of the support set. HPF-DCS is a combination of random and deterministic sampling. In random sampling, a number of predicted existing sub-particles are sampled from the prior pmf of the existing support set to handle the scenario when targets disappear randomly at a scan time. In deterministic sampling, the new support set corresponding to newly appearing targets is calculated by solving a sparsity promoting optimization problem. Our simulation results show that the proposed algorithm can track a time-varying number of targets successfully. It also outperforms the sequential Monte Carlo based probability hypothesis density (SMC-PHD) filter, as well as the multi-mode, multi-target track before detect (MM-MT-TBD) filter.https://ieeexplore.ieee.org/document/8962055/Multitarget trackingdynamic compressed sensingstate propagation based dynamic compressed sensing (SP-DCS)hybrid particle filter based dynamic compressed sensing (HPF-DCS)Doppler radar raw measurement based tracking |
| spellingShingle | Jing Liu Xiaoyu Jiang Xiaoqing Tian Mahendra Mallick Kaiyu Huang Chaoqun Ma Hybrid Particle Filter Based Dynamic Compressed Sensing for Signal-Level Multitarget Tracking IEEE Access Multitarget tracking dynamic compressed sensing state propagation based dynamic compressed sensing (SP-DCS) hybrid particle filter based dynamic compressed sensing (HPF-DCS) Doppler radar raw measurement based tracking |
| title | Hybrid Particle Filter Based Dynamic Compressed Sensing for Signal-Level Multitarget Tracking |
| title_full | Hybrid Particle Filter Based Dynamic Compressed Sensing for Signal-Level Multitarget Tracking |
| title_fullStr | Hybrid Particle Filter Based Dynamic Compressed Sensing for Signal-Level Multitarget Tracking |
| title_full_unstemmed | Hybrid Particle Filter Based Dynamic Compressed Sensing for Signal-Level Multitarget Tracking |
| title_short | Hybrid Particle Filter Based Dynamic Compressed Sensing for Signal-Level Multitarget Tracking |
| title_sort | hybrid particle filter based dynamic compressed sensing for signal level multitarget tracking |
| topic | Multitarget tracking dynamic compressed sensing state propagation based dynamic compressed sensing (SP-DCS) hybrid particle filter based dynamic compressed sensing (HPF-DCS) Doppler radar raw measurement based tracking |
| url | https://ieeexplore.ieee.org/document/8962055/ |
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