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

TL;DR

This paper develops sample-based methods to efficiently predict and optimize the GOSPA metric for non-myopic sensor control in complex target search and tracking scenarios, accounting for uncertainties like missed detections and false alarms.

Contribution

It introduces a novel sample-based approach for calculating the GOSPA metric and extends it to non-myopic sensor management using a Bellman recursion, handling complex scenarios.

Findings

Sample-based GOSPA prediction is computationally efficient.

Non-myopic planning improves target tracking performance.

Optimal plans align with intuitive strategies considering future observations.

Abstract

This paper is concerned with sensor management for target search and track using the generalised optimal subpattern assignment (GOSPA) metric. Utilising 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 paper, 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 non-myopic (i.e. multi-step) sensor management via the development of a Bellman-type recursion that optimises 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, non-myopic search and track algorithm offers a powerful mechanism for sensor management.