Decentralized Search and Track with Multiple Autonomous Agents

TL;DR

This paper introduces a decentralized cooperative approach using an augmented PHD filter for efficient search and tracking of multiple unknown targets by autonomous agents with limited sensing, adaptable to target births and deaths.

Contribution

It develops a novel decentralized search and tracking method that incorporates search density propagation, improving multi-target detection in dynamic environments.

Findings

Effective in detecting multiple targets with limited sensing.

Handles unknown target numbers and dynamic target appearances.

Demonstrated through simulation experiments.

Abstract

In this paper we study the problem of cooperative searching and tracking (SAT) of multiple moving targets with a group of autonomous mobile agents that exhibit limited sensing capabilities. We assume that the actual number of targets is not known a priori and that target births/deaths can occur anywhere inside the surveillance region. For this reason efficient search strategies are required to detect and track as many targets as possible. To address the aforementioned challenges we augment the classical Probability Hypothesis Density (PHD) filter with the ability to propagate in time the search density in addition to the target density. Based on this, we develop decentralized cooperative look-ahead strategies for efficient searching and tracking of an unknown number of targets inside a bounded surveillance area. The performance of the proposed approach is demonstrated through simulation experiments.