An Adaptive Target Tracking Method Utilizing Marginalized Cubature Kalman Filter with Uncompensated Biases

DENG Honggao; YU Runhua; JI Yuanfa; WU Sunyong; SUN Shaoshuai

doi:10.11999/JEIT240469

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DENG Honggao, YU Runhua, JI Yuanfa, WU Sunyong, SUN Shaoshuai. An Adaptive Target Tracking Method Utilizing Marginalized Cubature Kalman Filter with Uncompensated Biases[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240469

Citation:

DENG Honggao, YU Runhua, JI Yuanfa, WU Sunyong, SUN Shaoshuai. An Adaptive Target Tracking Method Utilizing Marginalized Cubature Kalman Filter with Uncompensated Biases[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240469

Citation:

DENG Honggao, YU Runhua, JI Yuanfa, WU Sunyong, SUN Shaoshuai. An Adaptive Target Tracking Method Utilizing Marginalized Cubature Kalman Filter with Uncompensated Biases[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240469

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An Adaptive Target Tracking Method Utilizing Marginalized Cubature Kalman Filter with Uncompensated Biases

doi: 10.11999/JEIT240469

DENG Honggao^{1, 4},
YU Runhua^{1, 2},
JI Yuanfa^{1, 2, 4},
WU Sunyong^{2, 3
,
,},
SUN Shaoshuai¹

1.
School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
2.
Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China
3.
School of Mathematics and Computing Science, Guilin University of Electronic Science and Technology, Guilin, 541004, China
4.
National & Local Joint Engineering Research Center of Satellite Navigation Positioning and Location Service, Guilin 541004, China

Funds: The National Natural Science Foundation of China (U23A20280, 62061010, 62161007), Guangxi Science and Technology Department Project(AB23026120)

Received Date: 2024-06-11
Rev Recd Date: 2024-12-02

Available Online: 2024-12-09

Abstract

Abstract

An adaptive target tracking method based on marginalized cubature Kalman filter is proposed for the target tracking problem in the presence of sensor measurement biases and unknown time-varying measurement noise. In this method, the constant measurement biases are eliminated by measurement differencing and the abrupt measurement biases can be identified by constructing the beta-Bernoulli indicator variable. The target states at adjacent moments are augmented to meet real-time filtering. The inverse Wishart distribution is used to model the covariance matrix of unknown measurement noise. Consequently, the joint distribution of the target state, indicator variable, and noise covariance matrix can be established, and the approximate posterior of each parameter is solved by variational Bayesian inference. To reduce the computational burden, the augmented state vector is marginalized, and the target tracking based marginal cubature Kalman filter is realized by combining with cubature Kalman filter. Simulation results demonstrate that the proposed method effectively handles the abrupt measurement biases and unknown time-varying measurement noise, achieving accurate target tracking.
- Abrupt measurement biases,
- Beta-Bernoulli distribution,
- Inverse Wishart distribution,
- Variational Bayesian inference,
- Marginalized cubature Kalman filter

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References(21)

References

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