Variational Bayesian Multiple Model Filter in the Presence of Outliers

被引：0
|
作者：
Cui, Tao [1 ]
Jing, Zhongliang [1 ]
Dong, Peng [1 ]
Leung, Henry [2 ]
Shen, Kai [3 ]
机构：
[1] Shanghai Jiao Tong Univ, Sch Aeronaut & Astronaut, Shanghai 200240, Peoples R China
[2] Univ Calgary, Dept Elect & Comp Engn, Calgary, AB T2N 1N4, Canada
[3] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 611756, Peoples R China
来源：
IEEE SENSORS JOURNAL | 2024年 / 24卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Gaussian scale mixture (GSM) distribution; multiple model (MM) filter; multivariate Laplace distribution; outliers; variational Bayesian (VB); MANEUVERING TARGET TRACKING; MULTITARGET TRACKING; BERNOULLI FILTER; STATE ESTIMATION; KALMAN FILTER; SYSTEMS;
D O I：
10.1109/JSEN.2024.3372502
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this article, we present a multiple model (MM) filter based on variational Bayesian (VB) in the presence of outliers. The multiple process and measurement models (M-PMMs) are taken into account to form a hybrid system, where the likelihood function is modeled as Gaussian scale mixture (GSM) distribution. The VB is employed to derive the general framework of state estimation. As two special cases of GSM, Gaussian distribution and multivariate Laplace (GML) distribution are used to make up measurement model sets and implement state estimation. The VB is applied to obtain a closed-form solution. Simulation results show the effectiveness of the proposed algorithm.
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页码：12985 / 12994
页数：10
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