Student-t mixture labeled multi-Bernoulli filter for multi-target tracking with heavy-tailed noise

Dong, Peng; Jing, Zhongliang; Leung, Henry; Shen, Kai; Wang, Jinran

doi:10.1016/j.sigpro.2018.06.014

Cited by 35 publications

(22 citation statements)

References 27 publications

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“…The proposed NNIWGG-J-GLMB filter is compared with the existing NGIWG-LMB filter [32], STM-LMB filter [33] and GM-J-GLMB filter [10]. Both the optimal sub-pattern assignment (OSPA) [40] and OSPA (2) (OSPA-on-OSPA) [18], [41] distances are utilized to evaluate the performance of all four filters.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Similarly, a novel CPHD filter for extended targets tracking with HTMN is presented in [31]. To obtain multitarget trajectories, a Gaussian (Normal) Gamma inverse Wishart Gamma distribution mixtures LMB (NGIWG-LMB) filter is proposed in [32] to perform MTT for stationary HTMN, as well as a Student's t mixture LMB (STM-LMB) filter for MTT with heavy-tailed process and measurement noises is presented in [33]. Furthermore, a novel LMB filter is presented in [34] for jump Markov systems to track multiple maneuvering targets under HTMN.…”

Section: Introductionmentioning

confidence: 99%

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Robust Generalized Labeled Multi-Bernoulli Filter for Multitarget Tracking With Unknown Non- Stationary Heavy-Tailed Measurement Noise

et al. 2021

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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.INDEX TERMS Generalized labeled multi-Bernoulli filter, multitarget tracking (MTT), variational Bayesian (VB), non-stationary, heavy-tailed measurement noise (HTMN), unknown and time-varying mean.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Generalized Labeled Multi-Bernoulli Filter for Multitarget Tracking With Unknown Non- Stationary Heavy-Tailed Measurement Noise

et al. 2021

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“…However, these methods need optimization or iteration procedure which may lead to a lot of extra computation. Recently, the robust Student-t filters for heavy-tailed process and measurement noises have been proposed in [19], [33]- [35] for single sensor. This method has less computational complexity, is easy to apply and can deal with high-dimensional problems.…”

Section: Measurement Vectormentioning

confidence: 99%

Distributed Consensus Student-t Filter for Sensor Networks With Heavy-Tailed Process and Measurement Noises

Wang¹,

Dong

Shen

et al. 2020

IEEE Access

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In the estimation of distributed sensor networks, process noise and measurement noise may have outliers which have heavy-tailed characteristics. To solve this problem, this paper proposes a distributed consensus estimating method for sensor networks based on Student-t distribution. In the state space model, both process noise and measurement noise are modeled as Student-t distributions with heavytailed characteristics. First, for the assumption that the process noise and measurement noise have the same degree of freedom parameters, an exact distributed consensus Student-t filtering algorithm is derived. In practical applications, this assumption is often not true, and due to the increasing degrees of freedom, the method will quickly converge to the traditional distributed consensus Kalman filter. Therefore, it is necessary to relax the assumption of the same degree of freedom and keep the degree of freedom unchanged within a certain range. Based on this, an approximate distributed consensus Student-t filter algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithm.

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“…In this case, the performance of conventional algorithms for the Gaussian measurement noise may be severely degraded due to the inconsistency of measurement noise statistics. Several studies applying the LMB filter to multi-object tracking under glint noise have been proposed [14], [15] to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

The Labeled Multi-Bernoulli Filter for Jump Markov Systems Under Glint Noise

Liu

Huang

2019

IEEE Access

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This paper proposes a novel labeled multi-Bernoulli (LMB) filter for jump Markov systems (JMS) to track the multiple maneuvering objects under glint noise. By modeling the glint noise as a Student's t-distribution and using the variational Bayesian method to acquire the approximate state distribution, we present an efficient implementation of the LMB filter with joint prediction and update for JMS. Simulation results illustrate that the proposed filter outperforms the existing filters for multi-object tracking under glint noise.INDEX TERMS Multi-object tracking, glint noise, labeled multi-Bernoulli filter, variational Bayesian method, jump Markov systems.

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Student-t mixture labeled multi-Bernoulli filter for multi-target tracking with heavy-tailed noise

Cited by 35 publications

References 27 publications

Robust Generalized Labeled Multi-Bernoulli Filter for Multitarget Tracking With Unknown Non- Stationary Heavy-Tailed Measurement Noise

Robust Generalized Labeled Multi-Bernoulli Filter for Multitarget Tracking With Unknown Non- Stationary Heavy-Tailed Measurement Noise

Distributed Consensus Student-t Filter for Sensor Networks With Heavy-Tailed Process and Measurement Noises

The Labeled Multi-Bernoulli Filter for Jump Markov Systems Under Glint Noise

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