Non-myopic GOSPA-driven Gaussian Bernoulli Sensor Management

Jones, George, García-Fernández, Ángel F and Blackman, Christian (2024) Non-myopic GOSPA-driven Gaussian Bernoulli Sensor Management IEEE Transactions on Aerospace and Electronic Systems, 60 (6). pp. 1-15. ISSN 0018-9251, 1557-9603

Text Jones_nonmyopic_final.pdf - Author Accepted Manuscript Available under License Creative Commons Attribution. Download (2MB) | Preview

Abstract

In this article, we propose an algorithm for nonmyopic sensor management for Bernoulli filtering, i.e., when there may be at most one target present in the scene. The algorithm is based on selecting the action that solves a Bellman-type minimization problem, whose cost function is the mean square generalized optimal subpattern assignment (GOSPA) error, over a future time window. We also propose an implementation of the sensor management algorithm based on an upper bound of the mean square GOSPA error and a Gaussian single-target posterior. Finally, we develop a Monte Carlo tree search algorithm to find an approximate optimal action within a given computational budget. The benefits of the proposed approach are demonstrated via simulations.

Item Type:	Article
Uncontrolled Keywords:	Planning, Filtering, Measurement, Target tracking, Cost function, Monte Carlo methods, Surveillance, Bernoulli filtering, Monte Carlo search tree, nonmyopic, sensor management
Depositing User:	Symplectic Admin
Date Deposited:	02 Jul 2024 09:48
Last Modified:	23 May 2026 08:56
DOI:	10.1109/taes.2024.3418750
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3182490
Disclaimer:	The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate.