Trajectory PHD Filter for Adaptive Measurement Noise Covariance Based on Variational Bayesian Approximation
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Published:2022-06-23
Issue:13
Volume:12
Page:6388
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Lu XingchenORCID,
Jing Dahai,
Jiang Defu,
Gao Yiyue,
Yang Jialin,
Li Yao,
Li Wendong,
Tao Jin,
Liu MingORCID
Abstract
In order to solve the problem that the measurement noise covariance may be unknown or change with time in actual multi-target tracking, this paper brings the variational Bayesian approximation method into the trajectory probability hypothesis density (TPHD) filter and proposes a variational Bayesian TPHD (VB-TPHD) filter to obtain measurement noise covariance adaptively. By modeling the unknown covariance as the random matrix that obeys the inverse gamma distribution, VB-TPHD filter minimizes the Kullback–Leibler divergence (KLD) and estimates the sequence of multi-trajectory states with noise covariance matrices simultaneously. We propose the Gaussian mixture VB-TPHD (AGM-VB-TPHD) filter under adaptive newborn intensity for linear Gaussian models and also give the extended Kalman (AEK-VB-TPHD) filter and unscented Kalman (AUK-VB-TPHD) filter in nonlinear Gaussian models. The simulation results prove the effectiveness of the idea that the VB-TPHD filter can form robust and stable trajectory filtering while learning adaptive measurement noise statistics. Compared with the tag-VB-PHD filter, the estimated error of the VB-TPHD filter is greatly reduced, and the estimation of the trajectory number is more accurate.
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference40 articles.
1. Advances in Statistical Multisource-Multitarget Information Fusion;Mahler,2014
2. Time-Matching Random Finite Set-Based Filter for Radar Multi-Target Tracking
3. Gamma Gaussian inverse-Wishart Poisson multi-Bernoulli filter for extended target tracking;Granström;Proceedings of the 19th International Conference on Information Fusion,2016
4. Bayesian Smoothing for the Extended Object Random Matrix Model
5. Markov Chain Monte Carlo Data Association for Multi-Target Tracking;Oh;IEEE Trans. Autom. Control.,2009
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