Markov chains for the prediction of tracking performance

Abstract

Highly accurate predictions of tracking performance usually require high fidelity Monte Carlo simulations that entail significant implementation time, run time, and complexity. In this paper we consider the use of Markov Chains as a simpler alternative that models critical aspects of the tracking process and provides reasonable estimates of tracking performance, while maintaining much lower cost and complexity. We describe a general procedure for Markov-Chain based performance prediction, and illustrate the use of this procedure in the context of an airborne system that employs a steerable EO/IR sensor to track single targets or multiple targets in non-overlapping fields of view. We discuss the effects of key model parameters, including measurement sampling rates, track termination, target occlusions, and missed detections. We also present plots of performance as a function of occlusion probability and target recognition probability that exemplify the use of the model.