Cooperative multi-target tracking via hybrid modeling and geometric optimization

Abstract

In this paper, we present a stochastic hybrid model of mobile networks able to encompass a large variety of multi-agent problems and phenomena. The model is applied to a case study where a heterogeneous mobile sensor network cooperatively detects and tracks mobile targets in the plane based on intermittent observations. When these observations form a satisfactory target trajectory, a mobile sensor is switched to pursuit mode and deployed to capture the target in minimum time. The mobile sensor network consists of a set of robotic sensors modeled as hybrid systems with processing capabilities. Since the sensors are installed on robotic platforms and have limited range, the geometry of the mobile sensors' field-of-view plays a critical role in motion planning and obstacle avoidance. The cost of operating the sensors is determined from the geometric properties of the network, its workspace and the probability of target detection. Simulation results verify the validity of the developed model and tracking methodology.