Optimal sensor selection for video-based target tracking in a wireless sensor network
The use of wireless sensor networks for target tracking is an active area of research. Imaging sensors that obtain video-rate images of a scene can have a significant impact in such networks, as they can measure vital information on the identity, position, and velocity of moving targets. Since wireless networks must operate under stringent energy constraints, it is important to identify the optimal set of imagers to be used in a tracking scenario such that the network lifetime is maximized. We formulate this problem as one of maximizing the information utility gained from a set of sensors subject to a constraint on the average energy consumption in the network. We use an unscented Kalman filter framework to solve the tracking and data fusion problem with multiple imaging sensors in a computationally efficient manner, and use a lookahead algorithm to optimize the sensor selection based on the predicted trajectory of the target. Simulation results show the effectiveness of this method of sensor selection.