Sensor reduction technique using Bellman optimal estimates of target agent dynamics

Abstract

A generalized sensor reduction technique is developed for a sensor network used in surveillance and target tracking operations. Reducing the number of sensors in the network leads to addressing immediate threats more quickly and lowering costs for acquiring and processing data. The methods in this work use Bellman optimality principles to estimate possible paths of an agent given an assumed environment model. These paths are then used to determine causal relationships between states in a surveillance field. By using this approach, a capture set is defined where the final states of trajectories are known using information from sensors located in other states. Sensors can then be removed from the network based on this capture set. This method is applied to a crowded hallway surveillance scenario where an agent may choose between two possible exits. The sensor network in this scenario determines if a target deviates from the crowd and moves toward an alternate exit. A proximity sensor grid is placed above the crowd to record the number of people that pass through the hallway. Our result shows that the Bellman optimal approximation of the capture set for the alternate exit identifies the region of the surveillance field where sensors are needed, allowing the others to be removed. Using the reduced sensor network, results are given that show the probability of a deviating agent becoming more distinct with respect to normal motion of the crowd. Therefore, we conclude that by incorporating a dynamic model of the agents' motion into the sensor network, sensors can be reduced, and increased detectability is noticed when sensors are removed early in a trajectory of interest.