Skip to Main Content
A framework is presented for information-theoretic sensor management for the detection of static targets. The sensor manager searches for targets within a cell grid using a suite of sensor platforms. Each sensor platform may contain one or more sensing modalities, and each of these modalities has known probabilities of detection and false alarm and also has an associated cost of use. Additional information such as motion constraints on the sensors and the prior distribution of the targets in space is incorporated. The sensor manager then directs the movement of the sensors through the grid by maximizing the expected information gain that will be obtained with each new sensor observation. Key modeling questions are addressed, including the selection of an appropriate information measure and the joint or independent management of the sensors. Through a number of simulations, the performance of the sensor manager is compared to the performance of a blind sweep procedure, a random search procedure, and an alternative information-theoretic sensor manager. The intelligent sensor management procedure is demonstrated to achieve a superior performance compared to all of the other three techniques. A specific application area for which the sensor management problem is becoming more critical is landmine detection; thus, the performance of the sensor manager is also analyzed using real data from three different landmine detection sensing modalities, and the proposed sensor management technique is again demonstrated to be superior compared to more simplistic approaches.