Robust Generalized Labeled Multi-Bernoulli Filter for Multitarget Tracking With Unknown Non- Stationary Heavy-Tailed Measurement Noise
| Autor: | Giuseppe Abreu, Xu Cong'an, Feng Lian, Liming Hou, Shuncheng Tan |
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| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Noise measurement non-stationary General Engineering Probability density function Filter (signal processing) variational Bayesian (VB) Upper and lower bounds Marginal likelihood TK1-9971 multitarget tracking (MTT) Noise Generalized labeled multi-Bernoulli filter Gamma distribution heavy-tailed measurement noise (HTMN) General Materials Science Electrical engineering. Electronics. Nuclear engineering Divergence (statistics) Algorithm unknown and time-varying mean Mathematics |
| Zdroj: | IEEE Access, Vol 9, Pp 94438-94453 (2021) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2021.3092021 |
| Popis: | A robust generalized labeled multi-Bernoulli (GLMB) filter is presented to perform multitarget tracking (MTT) with unknown non-stationary heavy-tailed measurement noise (HTMN). The HTMN is modeled as a multivariate Student’s t-distribution with unknown and time-varying mean. The proposed filter relaxes the restrictive assumption that the mean of HTMN is zero, and can effectively deal with MTT under the condition that the mean of HTMN is unknown and time-varying. The variational Bayesian (VB) approximation is applied in the GLMB filtering framework with the augmented state. The marginal likelihood function is obtained via minimizing the Kullback-Leibler divergence by the variational lower bound. The simulation results demonstrate that the proposed filter can effectively track multiple targets in both linear and nonlinear scenarios when the mean of HTMN is unknown and time-varying. |
| Databáze: | OpenAIRE |
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