Jointly-Optimized Searching and Tracking with Random Finite Sets

TL;DR

This paper presents a novel approach combining random finite set theory and Bayesian filtering to jointly search and track multiple mobile targets with limited sensing, using a genetic algorithm for optimization.

Contribution

It introduces a unified framework for joint searching and tracking using RFS and Bayesian filtering, formulated as a non-linear binary program optimized by genetic algorithms.

Findings

Effective in estimating number and states of targets

Simulations demonstrate improved search and tracking performance

Framework adaptable to various multi-target scenarios

Abstract

In this paper, we investigate the problem of joint searching and tracking of multiple mobile targets by a group of mobile agents. The targets appear and disappear at random times inside a surveillance region and their positions are random and unknown. The agents have limited sensing range and receive noisy measurements from the targets. A decision and control problem arises, where the mode of operation (i.e., search or track) as well as the mobility control action for each agent, at each time instance, must be determined so that the collective goal of searching and tracking is achieved. We build our approach upon the theory of random finite sets (RFS) and we use Bayesian multi-object stochastic filtering to simultaneously estimate the time-varying number of targets and their states from a sequence of noisy measurements. We formulate the above problem as a non-linear binary program (NLBP) and show that it can be approximated by a genetic algorithm. Finally, to study the effectiveness and performance of the proposed approach we have conducted extensive simulation experiments.