A Closed-Form Prediction Update for Extended Target Tracking Using Random Matrices

Title: A Closed-Form Prediction Update for Extended Target Tracking Using Random Matrices
Creator: Bartlett, Nathan James; Renton, Christopher; Wills, Adrian G.
Relation: IEEE Transactions on Signal Processing Vol. 68, Issue 2020, p. 2404-2418
Publisher Link: http://dx.doi.org/10.1109/TSP.2020.2984390
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Resource Type: journal article
Date: 2020
Description: This paper proposes a new class of state transition models that afford closed-form predictions for the tracking of extended targets. A key innovation is to employ a non-central inverse Wishart distribution to model the state transition density of the target extent. Importantly, this results in a simplified prediction update that is computationally efficient and improves target tracking performance when compared to state-of-the-art alternatives on standard simulation scenarios.
Subject: extended target; random matrix model; inverse wishart; non-central inverse wishart
Identifier: http://hdl.handle.net/1959.13/1439139
Identifier: uon:40831
Identifier: ISSN:1053-587X
Language: eng
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