Autonomous Bayesian Search and Tracking, and its Experimental Validation

Abstract

We present a technique that uniformly controls a team of autonomous sensor platforms charged with the dual task of searching for and then tracking a moving target within a recursive Bayesian estimation framework. The proposed technique defines the target detectable region, and uniformly formulates observation likelihoods with detection and no-detection events. The unified likelihood function allows the proposed technique to update and maintain the target belief, regardless of the target detectability. For unified search and tracking (SAT), the proposed technique further predicts the belief in a finite-time horizon, and decides control actions by maximizing a unified objective function consisting of local and global measures derived from the predicted belief. Using the objective function, the proposed technique can smoothly change its control actions even during transitions between SAT. The numerical results first show successful SAT by the proposed technique in tests using a sensor platform with different detectability and comparison with conventional searching techniques under different prior knowledge, and then identifies the superiorities of the proposed technique in SAT. The experimental results finally validate the applicability and extendability of the proposed technique via coordinated SAT in a field experiment.