Open AccessTracking multiple extended targets with multi‐Bernoulli filter
Author(s) -
Hu Qi,
Ji Hongbing,
Zhang Yongquan
Publication year - 2019
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2018.5125
Subject(s) - filter (signal processing) , inverse wishart distribution , bernoulli's principle , computer science , algorithm , inverse filter , cardinality (data modeling) , recursion (computer science) , control theory (sociology) , tracking (education) , kernel adaptive filter , gaussian , filter design , adaptive filter , mathematics , inverse , artificial intelligence , computer vision , wishart distribution , machine learning , data mining , engineering , psychology , control (management) , physics , aerospace engineering , geometry , quantum mechanics , multivariate statistics , pedagogy
This study presents an improved multi‐target multi‐Bernoulli (IMeMBer) gamma Gaussian inverse Wishart (GGIW) filter for tracking multiple extended targets (ETs). The main contribution of this study consists of three parts, first, a novel method is proposed to obtain the unbiased cardinality estimation of multiple targets using the multi‐Bernoulli recursion. As a variation of the existing cardinality‐balanced MeMBer (CBMeMBer) filter, the presented filter is called the improved MeMBer filter, which overcomes the high detection probability limitation of the CBMeMBer filter. Second, based on the mathematical derivation, the IMeMBer filter is expanded to accommodate the characteristics of the ETs of which each target generates more than one measurement at each time step, and the GGIW method is used for its implementation. The resulting filter simultaneously provides the kinematic, extended and measurement rate states of ETs with an unknown and time‐varying number. Third, the simulation results show that the presented filter achieves a considerable performance at the cost of less time, compared to the labelled multi‐Bernoulli GGIW filter.