Hybrid Particle Filter Based Dynamic Compressed Sensing for Signal-Level Multitarget Tracking

Jing Liu; Xiaoyu Jiang; Xiaoqing Tian; Mahendra Mallick; Kaiyu Huang; Chaoqun Ma

doi:10.1109/ACCESS.2020.2967550

IEEE Access (Jan 2020)

Hybrid Particle Filter Based Dynamic Compressed Sensing for Signal-Level Multitarget Tracking

Jing Liu,
Xiaoyu Jiang,
Xiaoqing Tian,
Mahendra Mallick,
Kaiyu Huang,
Chaoqun Ma

Affiliations

Jing Liu: ORCiD; Ministry of Education Key Laboratory of Intelligent and Network Security, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, China
Xiaoyu Jiang: Department of Information Communication, Army Academy of Armored Forces, Beijing, China
Xiaoqing Tian: Ministry of Education Key Laboratory of Intelligent and Network Security, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, China
Mahendra Mallick: Electrical Engineering Department, University of Colorado, Boulder, CO, USA
Kaiyu Huang: Ministry of Education Key Laboratory of Intelligent and Network Security, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, China
Chaoqun Ma: ORCiD; Ministry of Education Key Laboratory of Intelligent and Network Security, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, China

DOI: https://doi.org/10.1109/ACCESS.2020.2967550
Journal volume & issue: Vol. 8
pp. 17134 – 17148

Abstract

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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.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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