SENSOR SCHEDULING AND EFFICIENT ALGORITHM IMPLEMENTATION FOR TARGET TRACKING

Recent advances in sensor technology coupled with embedded systems and wireless networking has made it possible to deploy sensors for numerous applications including target tracking, environmental science, defense information, and security. Sensor scheduling, a process to allocate sensing resources by optimizing a performance metric over a future time-horizon under constraints, is an effective method to improve performance for such problems. This work investigates myopic (one step ahead) and non-myopic (multiple steps ahead) sensor scheduling algorithms for target tracking applications. Two methods of predicting tracker performance are developed that can be used for tar-get tracking applications. The first is covariance-based, and it can be used with covariance-based scheduler costs. The second is unscented transform-based and it can be used with arbitrary scheduler costs. In application, both methods give a significant improvement in tracking performance over the tracking performance without sensor scheduling. The use of non-myopic sensor scheduling is often restricted due to an exponential dependency of computational and memory requirements on the length of prediction horizon. Fo