TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Hoher, Patrick
A1  - Baur, Tim
A1  - Wirtensohn, Stefan
A1  - Reuter, Johannes
T1  - A Detection Driven Adaptive Birth Density for the Labeled Multi-Bernoulli Filter
T2  - 23rd International Conference on Information Fusion (FUSION), 6-9 July 2020, Rustenburg, South Africa, virtual
N2  - 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.
KW  - Data Fusion
KW  - Multi Bernoulli Filter
KW  - Birth Density
KW  - Adaptive
KW  - Rauch-Tung-Striebel Recursion
Y1  - 2020
SN  - 978-0-578-64709-8
SS  - 978-0-578-64709-8
U6  - https://doi.org/10.23919/FUSION45008.2020.9190532
DO  - https://doi.org/10.23919/FUSION45008.2020.9190532
N1  - Volltextzugriff für Angehörige der Hochschule Konstanz via Datenbank IEEE Xplore möglich
SP  - 8
S1  - 8
PB  - IEEE
ER  -