Adaptive Mobile Sensor Positioning for Multi-Static Target Tracking

Unmanned air vehicles (UAVs) are playing an increasingly prominent role in both military and civilian applications. We focus here on the use of multiple UAV agents in a target tracking application where performance is improved by exploiting each agent's maneuverability. Local time-delay and Doppler measurements made at each UAV are used as inputs to an extended Kalman filter (EKF) which tracks the target's position and velocity. Two simple metrics are defined to quantify the accuracy of the tracking algorithm, and heading feedback to the UAVs is used to minimize the metric and improve tracking performance. A simplified version of one of the algorithms that reduces computational complexity is also presented. Simulations demonstrate the significant improvement that results when the UAV sensors are allowed to be optimally positioned during tracking.