A robust random matrix prediction model for extended object rotations

Title: A robust random matrix prediction model for extended object rotations
Creator: Bartlett, Nathan J.; Wills, Adrian G.
Relation: 2021 IEEE 24th International Conference on Information Fusion (FUSION 2021). Proceedings of 2021 24th International Conference on Information Fusion (FUSION): Fusion 2021 (Sun City, South Africa 01-04 November, 2021)
Publisher Link: http://dx.doi.org/10.23919/FUSION49465.2021.9626919
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Resource Type: conference paper
Date: 2021
Description: This paper proposes a robust random matrix prediction model for the tracking of extended objects performing turn-rate manoeuvres. A key innovation is to derive exact solutions for two matrix-valued expectations that are currently approximated by third-order Taylor series expansions. Moreover, we prove that the use of such Taylor series expansions will lead to tracking failure when the turn-rate variance exceeds a known threshold. The exact solution can be computed in a robust and computationally efficient manner, and we demonstrate its utility in simulation compared with state-of-the-art alternative approaches.
Subject: extended object tracking; Bayesian filtering; random matrix; non-central inverse Wishart
Identifier: http://hdl.handle.net/1959.13/1431541
Identifier: uon:38971
Identifier: ISBN:9781737749714
Language: eng
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