Posterior Cramér–Rao lower bounds for extended target tracking with PMBM conjugate recursion

Autor:	Xingxiang Xie, Xiongwei Zhao, Zhumei Song, Kening Li
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	signal processing tracking tracking filters Electrical engineering. Electronics. Nuclear engineering TK1-9971
Zdroj:	Electronics Letters, Vol 60, Iss 18, Pp n/a-n/a (2024)
Druh dokumentu:	article
ISSN:	1350-911X 0013-5194
DOI:	10.1049/ell2.70041
Popis:	Abstract This letter considers the posterior Cramér–Rao lower bounds (PCRLB) problem for extended target tracking from a stack of measurement data that are modelled as random variables in the random finite sets framework. The scalars in the traditional PCRLB are converted into vectors based on random finite sets to derive a theoretical lower bound. In this way, the proposed method can be applied to the multi‐target tracking problem and accommodates scenarios with targets of varying. Moreover, solving the data association problem from four parts caused by the conjugate update of the Poisson multi‐Bernoulli mixture filter is considered. Simulation results are presented to verify the effectiveness of the derived PCRLB.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/7f57edcd9a0d48fd8552e330a9887ae5 Zobrazit plný text záznamu Plný text View record in DOAJ