On Decentralized Classification using a Network of Mobile Sensors

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Timothy H. Chung, Joel W. Burdick, Richard M. Murray
Submitted, 2005 International Conference on Robotics and Automation

This paper considers how a team of mobile sensors should cooperatively move so as to optimally categorize a single moving target from their noisy sensor readings. The cooperative control procedure is based on the development of a cost function that quantifies the team’s classification error. The robots’ motions are then chosen to minimize this function. We particularly investigate the case where the sensor noise and class distributions are Gaussian. In this case, we can derive a duality principle which states that optimal classification will be realized when the covariance of the target estimate is minimized. That is, in this case, optimal estimation leads naturally to optimal classification. We extend previous work to develop a distributed discrete-gradient search algorithm that guides the team’s location motions for purposes of optimal estimation and classification. The concepts developed are validated through numerical studies.