JOURNAL ARTICLE

A Variational Bayesian Based Strong Tracking Interpolatory Cubature Kalman Filter for Maneuvering Target Tracking

Jian WangTao ZhangXiang XuYao Li

Year: 2018 Journal:   IEEE Access Vol: 6 Pages: 52544-52560   Publisher: Institute of Electrical and Electronics Engineers

Abstract

In tracking a maneuverable target, a proper estimation method with better filtering accuracy, stronger robustness, and faster convergence speed is crucial to the tracking system. The performance of conventional nonlinear Gaussian approximate filters may decline when the target engages in abrupt state changes or the noise covariance matrix is unknown and time-varying. In order to overcome these problems, a new variational Bayesian-based strong tracking interpolatory cubature Kalman filter (VB-STICKF) is deduced in this paper. Gaussian weighted integrals in the nonlinear filter are performed using the interpolatory cubature rule, which has better numerical characteristics for maneuvering target tracking. Moreover, by introducing the strong tracking filter into ICKF, the fading factor is used to modify the predicted error covariance, and the residual sequences are forced to be orthogonal, thus the decreasing performance resulted by the states change and the uncertain process noise could be effectively prevented. Furthermore, the measurement noise can be estimated online by variational Bayesian approach based on the inverse wishart distribution, the robustness of dealing with the uncertain measurement noise is improved. The detailed derivation of VB-STICKF for the general nonlinear models is presented in the paper. A target tracking problem with model uncertainty and time-varying process and measurement noise is utilized to test the performance of the proposed filter, the experimental results of three different scenarios demonstrate the improved filtering performance of the deduced VB-STICKF algorithm.

Keywords:
Kalman filter Robustness (evolution) Covariance Control theory (sociology) Ensemble Kalman filter Computer science Nonlinear system Extended Kalman filter Mathematics Algorithm Covariance matrix Invariant extended Kalman filter Noise (video) Filter (signal processing) Artificial intelligence Computer vision Statistics

Metrics

38
Cited By
4.37
FWCI (Field Weighted Citation Impact)
40
Refs
0.94
Citation Normalized Percentile
Is in top 1%
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Citation History

Topics

Target Tracking and Data Fusion in Sensor Networks
Physical Sciences →  Computer Science →  Artificial Intelligence
Inertial Sensor and Navigation
Physical Sciences →  Engineering →  Aerospace Engineering
Maritime Navigation and Safety
Physical Sciences →  Engineering →  Ocean Engineering
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