Information-based sensor management for landmine detection using multimodal sensors

We consider an information-theoretic approach for sensor management that chooses sensors and sensor parameters in order to maximize the expected discrimination gain associated with each new sensor measurement. We analyze the problem of searching for N targets with M multimodal sensors, where each sensor has its own probability of detection, probability of false alarm, and cost of use. Other information, such as the prior distribution of the targets in space and the degree of constraint of the sensor motion, is also utilized in our formulation. Performance of the sensor management algorithm is then compared to the performance of a direct-search procedure in which the sensors blindly search through all cells in a predetermined path. The information-based sensor manager is found to have significant performance gains over the direct-search approach. Algorithm performance is also analyzed using real landmine data taken with three different sensing modalities. Detection performance using the sensor management algorithm is again found to be superior to detection performance using a blind search procedure. The simulation and real-data results also both illuminate the increased performance available through multimodal sensing.