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A Detection Driven Adaptive Birth Density for the Labeled Multi-Bernoulli Filter

  • Modeling a suitable birth density is a challenge when using Bernoulli filters such as the Labeled Multi-Bernoulli (LMB) filter. The birth density of newborn targets is unknown in most applications, but must be given as a prior to the filter. Usually the birth density stays unchanged or is designed based on the measurements from previous time steps. In this paper, we assume that the true initial state of new objects is normally distributed. The expected value and covariance of the underlying density are unknown parameters. Using the estimated multi-object state of the LMB and the Rauch-Tung-Striebel (RTS) recursion, these parameters are recursively estimated and adapted after a target is detected. The main contribution of this paper is an algorithm to estimate the parameters of the birth density and its integration into the LMB framework. Monte Carlo simulations are used to evaluate the detection driven adaptive birth density in two scenarios. The approach can also be applied to filters that are able to estimate trajectories.

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Metadaten
Author:Patrick Hoher, Tim Baur, Stefan Wirtensohn, Johannes ReuterORCiD
DOI:https://doi.org/10.23919/FUSION45008.2020.9190532
ISSN:978-0-578-64709-8
Parent Title (English):23rd International Conference on Information Fusion (FUSION), 6-9 July 2020, Rustenburg, South Africa, virtual
Publisher:IEEE
Document Type:Conference Proceeding
Language:English
Year of Publication:2020
Release Date:2021/01/15
Tag:Adaptive; Birth Density; Data Fusion; Multi Bernoulli Filter; Rauch-Tung-Striebel Recursion
Page Number:8
Note:
Volltextzugriff für Angehörige der Hochschule Konstanz via Datenbank IEEE Xplore möglich
Institutes:Institut für Systemdynamik - ISD
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (English):License LogoLizenzbedingungen IEEE