Cardinalised probability hypothesis density tracking algorithm for extended objects with glint noise

Li, Cuiyun; Wang, Rong; Hu, Yu-Hen; Wang, Jinbin

doi:10.1049/iet-smt.2016.0004

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…In [28], the glint noise was modeled by the mixture of a Gaussian distribution and a Laplacian distribution. In particular, the ST distribution is immune to measurement outliers and has been widely used in RFS-based MTT [29][30][31], which can accurately characterize the tailed behavior by carefully selecting its degree of freedom (DOF) parameter. Based on the ST distribution and variational Bayesian (VB) method [32], a robust MMTT algorithm was proposed under the marginal distribution Bayes (MDB) filtering framework [33].…”

Section: Introductionmentioning

confidence: 99%

A Robust Interacting Multi-Model Multi-Bernoulli Mixture Filter for Maneuvering Multitarget Tracking under Glint Noise

Yu,

Gu,

2024

Sensors

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In practical radar systems, changes in the target aspect toward the radar will result in glint noise disturbances in the measurement data. The glint noise has heavy-tailed characteristics and cannot be perfectly modeled by the Gaussian distribution widely used in conventional tracking algorithms. In this article, we investigate the challenging problem of tracking a time-varying number of maneuvering targets in the context of glint noise with unknown statistics. By assuming a set of models for the possible motion modes of each single-target hypothesis and leveraging the multivariate Laplace distribution to model measurement noise, we propose a robust interacting multi-model multi-Bernoulli mixture filter based on the variational Bayesian method. Within this filter, the unknown noise statistics are adaptively learned while filtering and the predictive likelihood is approximately calculated by means of the variational lower bound. The effectiveness and superiority of our proposed filter is verified via computer simulations.

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Section: Introductionmentioning

confidence: 99%

A Robust Interacting Multi-Model Multi-Bernoulli Mixture Filter for Maneuvering Multitarget Tracking under Glint Noise

Yu,

Gu,

2024

Sensors

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“…Li proposed robust PHD filter based on Student‐ t distribution and has exploited the Variational Bayesian (VB) method to deal with heavy‐tailed noise in the PHD filter framework [41]. Then, it is extended to CPHD filter to deal with heavy‐tailed noise and extended target tracking [42]. However, these methods can only deal with heavy‐tailed measurement noise.…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive δ‐generalised labelled multi‐Bernoulli filter for multi‐target tracking with heavy‐tailed noise

Jing

2022

IET Radar Sonar & Navi

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Conventional δ‐generalised labelled multi‐Bernoulli filter (δ‐GLMB) cannot deal with the problem of the heavy‐tailed process noise and measurement noise. In order to solve this problem, an adaptive δ‐GLMB approach based on minimising Kullback‐Leibler Divergence (KLD) is proposed in this study. The inverse wishart and Student‐t mixture is used to approximate the joint posterior distribution of process noise covariance and measurement noise covariance together with multi‐target state, and the multi‐target state and noise parameters are jointly estimated by minimising the KLD. The simulation results show that the proposed approach is robust to multi‐target tracking for condition with heavy‐tailed process noise covariance and measurement noise covariance. Accurate target number and target state estimation are obtained by effectively estimating the process noise covariance and measurement noise covariance.

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“…The variational Bayesian (VB) approach is utilized to deal with significant intractability caused by the Student's t-distribution in the PHD filtering framework. 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].…”

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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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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Cardinalised probability hypothesis density tracking algorithm for extended objects with glint noise

Cited by 13 publications

References 26 publications

A Robust Interacting Multi-Model Multi-Bernoulli Mixture Filter for Maneuvering Multitarget Tracking under Glint Noise

A Robust Interacting Multi-Model Multi-Bernoulli Mixture Filter for Maneuvering Multitarget Tracking under Glint Noise

A novel adaptive δ‐generalised labelled multi‐Bernoulli filter for multi‐target tracking with heavy‐tailed noise

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

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