Managing multiple sensor resources using covariance control techniques for tracking systems with data association

As the profusion of different sensors improves the capabilities of tracking platforms, tracking objectives can move from simply trying to achieve the most with a limited sensor suite to developing the ability to achieve more specific tracking goals, such as reducing the uncertainty in a target estimate enough to accurately fire a weapon at a target or to ensure that a mobile robot does not collide with an obstacle. Multisensor manager systems that balance tracking performance with system resources have traditionally been ill-suited for achieving such specific control objectives. This thesis extends the methods developed in single-sensor management schemes to a multisensor application using an approach known as Covariance Control, which selects sensor combinations based on the difference between the desired covariance matrix and that of the current estimated covariance. Several implementational issues are addressed including the possible delay of sensor requests and the computational demand of the algorithms. The approach is then extended to include the effects of multiple measurements due to clutter or closely-spaced targets. Monte Carlo simulations are used to demonstrate the performance of these techniques.