Trajectory PHD Filter for Adaptive Measurement Noise Covariance Based on Variational Bayesian Approximation

In order to solve the problem that the measurement noise covariance may be unknown or change with time in actual multi-target tracking, this paper brings the variational Bayesian approximation method into the trajectory probability hypothesis density (TPHD) filter and proposes a variational Bayesian...

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主要な著者:	Xingchen Lu, Dahai Jing, Defu Jiang, Yiyue Gao, Jialin Yang, Yao Li, Wendong Li, Jin Tao, Ming Liu
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2022-06-01
シリーズ:	Applied Sciences
主題:	trajectory PHD filter variational Bayesian approximation noise covariance matrix inverse Gamma distribution estimation of trajectory
オンライン･アクセス:	https://www.mdpi.com/2076-3417/12/13/6388
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