Improved Adaptive Kalman Filter With Unknown Process Noise Covariance

被引：0

作者：

Ma, Jirong ^{[1
,2
]}

Lan, Hua ^{[1
,2
]}

Wang, Zengfu ^{[1
,2
]}

Wang, Xuezhi ^{[3
]}

Pan, Quan ^{[1
,2
]}

Moran, Bill ^{[4
]}

机构：

[1] Northwestern Polytech Univ, Sch Automat, Xian, Shaanxi, Peoples R China

[2] Minist Educ, Key Lab Informat Fus Technol, Xian, Shaanxi, Peoples R China

[3] RMIT Univ, Sch Engn, Melbourne, Vic, Australia

[4] Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic, Australia

来源：

2018 21ST INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) | 2018年

基金：

中国国家自然科学基金;

关键词：

Adaptive filtering; unknown process noise covariance; variational Bayesian; conjugate priors;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper considers the joint recursive estimation of the dynamic state and the time-varying process noise covariance for a linear state space model. The conjugate prior on the process noise covariance, the inverse Wishart distribution, provides a latent variable. A variational Bayesian inference framework is then adopted to iteratively estimate the posterior density functions of the dynamic state, process noise covariance and the introduced latent variable. The performance of the algorithm is demonstrated with simulated data in a target tracking application.

引用

页码：2054 / 2058

页数：5

共 50 条

[21] Hybrid Adaptive Cubature Kalman Filter with Unknown Variance of Measurement Noise
Shi, Yuepeng
Tang, Xianfeng
Feng, Xiaoliang
Bian, Dingjun
Zhou, Xizhao
[J]. SENSORS, 2018, 18 (12)
[22] Online Adaptive Kalman Filter for Target Tracking with Unknown Noise Statistics
Chen, Yuming
Li, Wei
Wang, Yuqiao
[J]. IEEE Sensors Letters, 2021, 5 (03):
[23] Design of parallel adaptive extended Kalman filter for online estimation of noise covariance
Xiong, Kai
Liu, Liangdong
[J]. AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY, 2019, 91 (01): : 112 - 123
[24] Adaptive system noise covariance for performance enhancement of Kalman filter based algorithms
Lee, V
Chan, KCC
Leung, H
[J]. ACQUISITION, TRACKING, AND POINTING X, 1996, 2739 : 304 - 318
[25] A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance
Zheng, Binqi
Fu, Pengcheng
Li, Baoqing
Yuan, Xiaobing
[J]. SENSORS, 2018, 18 (03):
[26] Robust Kalman Filters With Unknown Covariance of Multiplicative Noise
Yu, Xingkai
Meng, Ziyang
[J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2024, 69 (02) : 1171 - 1178
[27] Designing an Extended Kalman Filter for Estimating Speed and Flux of an Induction Motor with Unknown Noise Covariance
Lee, Daniel C.
[J]. 2016 IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE), 2016,
[28] Adaptive fractional-order unscented Kalman filter with unknown noise statistics
Xiao, Kui
Yu, Wentao
Qu, Feng
Lian, Jianfang
Liu, Chaofan
Liu, Weirong
[J]. INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2022, 36 (10) : 2519 - 2536
[29] Variational adaptive Kalman filter for unknown measurement loss and inaccurate noise statistics
Fu, Hongpo
Cheng, Yongmei
[J]. SIGNAL PROCESSING, 2023, 212
[30] Adaptive Unscented Kalman Filter for Online State, Parameter, and Process Covariance Estimation
Riva, Mauro Hernan
Dagen, Matthias
Ortmaier, Tobias
[J]. 2016 AMERICAN CONTROL CONFERENCE (ACC), 2016, : 4513 - 4519

← 1 2 3 4 5 →