Systems Engineering and Electronics
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YUAN Changshun, WANG Jun, XIANG Hong, SUN Jinping
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Abstract:
The multi-target tracking methods based on δ-generalized labeled multi-Bernoulli (δ-GLMB) filter usually assume that the measurement noise covariance is known a priori. This is unrealistic for real applications, as it may be previously unknown or its value may be time-varying as the environment changes. To solve this problem, an adaptive δ-GLMB filtering algorithm based on variational Bayesian (VB) approximation is proposed. Based on the δ-GLMB filter, the proposed algorithm approximates joint posterior density of the measurement noise covariance and multitarget states by the mixture distribution of the products of inverse Wishart and Gaussian, and derives filtering iteration by the VB approximation. Simulation results indicate that the proposed algorithm has a strong robustness and could effectively estimate the measurement noise covariance and the number of targets as well as the corresponding multitarget states under the unknown measurement noise covariance scenario.
YUAN Changshun, WANG Jun, XIANG Hong, SUN Jinping. Adaptive δ-GLMB filtering algorithm based on VB approximation[J]. Systems Engineering and Electronics, doi: 10.3969/j.issn.1001-506X.2017.02.01.
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URL: https://www.sys-ele.com/EN/10.3969/j.issn.1001-506X.2017.02.01
https://www.sys-ele.com/EN/Y2017/V39/I2/237