Robust Labeled Multi-Bernoulli Filter with Inaccurate Noise Covariances

Lian, Yiru; Lian, Feng; Hou, Liming

doi:10.23919/fusion49751.2022.9841248

Cited by 2 publications

(2 citation statements)

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“…It has been widely used in civilian and military fields such as autonomous driving, air traffic control, missile warning, etc. In the past two decades, a new random finite set (RFS)-based MTT strategy, which models the multi-target state and sensor measurement as two individual RFSs, has been developed to avoid the data association required by the traditional MTT strategy [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…The variational Bayesian (VB) inference provides a solution to this problem. This approach has been widely used for estimating the unknown covariance [3,[17][18][19], in which the VB-LMB [18] and Gaussian inverse-Wishart (IW) mixture LMB (GIWM-LMB) [19] filters were proposed by modeling the unknown measurement covariance as inverse-Gamma and IW distributions, respectively. Using a combination of the VB approach and Student's t-model, a filter called the VB Kalman filter (VB-KF) [20,21] for single-target tracking (STT) under heavy-tailed measurement noise is proposed.…”

Section: Introductionmentioning

confidence: 99%

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Student’s t-Based Robust Poisson Multi-Bernoulli Mixture Filter under Heavy-Tailed Process and Measurement Noises

Zhu,

Xie,

Liu

2023

Remote Sensing

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A novel Student’s t-based robust Poisson multi-Bernoulli mixture (PMBM) filter is proposed to effectively perform multi-target tracking under heavy-tailed process and measurement noises. To cope with the common scenario where the process and measurement noises possess different heavy-tailed degrees, the proposed filter models this noise as two Student’s t-distributions with different degrees of freedom. Furthermore, this method considers that the scale matrix of the one-step predictive probability density function is unknown and models it as an inverse-Wishart distribution to mitigate the influence of heavy-tailed process noise. A closed-form recursion of the PMBM filter for propagating the approximated Gaussian-based PMBM posterior density is derived by introducing the variational Bayesian approach and a hierarchical Gaussian state-space model. The overall performance improvement is demonstrated through three simulations.

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Section: Introductionmentioning

confidence: 99%