Non-Myopic Sensor Control for Target Search and Track Using a Sample-Based GOSPA Implementation

Hernandez, Marcel ORCID: 0000-0003-4224-2908, García-Fernández, Ángel F and Maskell, Simon ORCID: 0000-0003-1917-2913 (2023) Non-Myopic Sensor Control for Target Search and Track Using a Sample-Based GOSPA Implementation. IEEE Transactions on Aerospace and Electronic Systems, 60 (1). pp. 1-17.

PDF
AES_manuscript_2col_final_Oct_2023.pdf - Author Accepted Manuscript
Download (3MB) | Preview

Official URL: http://dx.doi.org/10.1109/taes.2023.3324908

Abstract

This article is concerned with sensor management for target search and track using the generalized optimal subpattern assignment (GOSPA) metric. Utilizing the GOSPA metric to predict future system performance is computationally challenging, because of the need to account for uncertainties within the scenario, notably the number of targets, the locations of targets, and the measurements generated by the targets subsequent to performing sensing actions. In this article, efficient sample-based techniques are developed to calculate the predicted mean square GOSPA metric. These techniques allow for missed detections and false alarms, and thereby enable the metric to be exploited in scenarios more complex than those previously considered. Furthermore, the GOSPA methodology is extended to perform nonmyopic (i.e., multistep) sensor management via the development of a Bellman-type recursion that optimizes a conditional GOSPA-based metric. Simulations for scenarios with missed detections, false alarms, and planning horizons of up to three time steps demonstrate the approach, in particular showing that optimal plans align with an intuitive understanding of how taking into account the opportunity to make future observations should influence the current action. It is concluded that the GOSPA-based, nonmyopic search and track algorithm offers a powerful mechanism for sensor management.

Item Type:	Article
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	18 Oct 2023 09:04
Last Modified:	15 Mar 2024 08:52
DOI:	10.1109/taes.2023.3324908
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3173846