Robust adaptive multi‐target tracking with unknown heavy‐tailed noise. Issue 2 (15th November 2022)

• Record Type: Journal Article
• Authors: Gu, Peng; Jing, Zhongliang; Wu, Liangbin
• Is Part Of: IET signal processing. Volume 17:Issue 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-15
• Subjects: Gaussian approximation -- heavy‐tailed noise -- multi‐target tracking -- variational Bayesian approach; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/sil2.12171
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract: In multi‐target tracking, non‐Gaussian heavy‐tailed process noise (PN) and measurement noise (MN) are introduced by unknown manoeuvring and noise‐corrupted measurements. This study proposes a Gaussian approximation approach based on multivariate Student‐ t distribution, which is designed to characterise non‐Gaussian heavy‐tailed MN covariance and PN covariance. The variational Bayesian approach is applied to a generalised labelled multi‐Bernoulli (GLMB) with an augmented state, and a robust adaptive generalised labelled multi‐Bernoulli (RAGLMB) framework is derived to recursively propagate the joint posterior density of noise covariance and target state. The simulation results indicate that the proposed RAGLMB filter is robust to targets affected by non‐Gaussian heavy‐tailed PN and MN. Abstract : A robust adaptive generalised labelled multi‐Bernoulli (RAGLMB) framework is derived to recursively propagate the joint posterior density of noise covariance and target state. The proposed RAGLMB filter is robust to targets affected by non‐Gaussian heavy‐tailed process noise and measurement noise.