A GEOMETRIC OPTIMIZATION APPROACH TO DETECTING AND INTERCEPTING DYNAMIC TARGETS USING A MOBILE SENSOR NETWORK

A methodology is developed to deploy a mobile sensor network for the purpose of detecting and capturing mobile targets in the plane. The sensing-pursuit problem considered in this paper is analogous to the Marco Polo game, in which a pursuer Marco must capture multiple mobile targets that are sensed intermittently, and with very limited information. The competing objectives exhibited by this problem arise in a number of surveillance and monitoring applications. In this paper, the mobile sensor network consists of a set of robotic sensors that must track and capture mobile targets based on the information obtained through cooperative detections. When these detections form a satisfactory target track, a mobile sensor is switched to pursuit mode and deployed to capture the target in minimum time. Since the sensors are installed on robotic platforms and have limited range, the geometry of the platforms and of the sensors' fields-of-view play a key role in obstacle avoidance and target detection. A new cell-decomposition approach is presented to determine the probability of detection and the cost of operating the sensors from the geometric properties of the network and its workspace. The correctness and complexity of the algorithm are analyzed, proving that the termination time is a function of the network parameters and of the number of required detections.