ISEE.U: Distributed online active target localization with unpredictable targets
This addresses the problem of efficient and robust target localization in distributed systems for applications like surveillance or robotics, though it is incremental as it builds on existing active learning and distributed computation methods.
This paper tackles the problem of target localization with an online active learning algorithm that uses distributed, parameter-free computations to maximize localization accuracy for unpredictable targets, achieving results asymptotically equal to a centralized maximum-likelihood estimator. It outperforms a state-of-the-art algorithm for unpredictable target movements with 100 times less computation time.
This paper addresses target localization with an online active learning algorithm defined by distributed, simple and fast computations at each node, with no parameters to tune and where the estimate of the target position at each agent is asymptotically equal in expectation to the centralized maximum-likelihood estimator. ISEE.U takes noisy distances at each agent and finds a control that maximizes localization accuracy. We do not assume specific target dynamics and, thus, our method is robust when facing unpredictable targets. Each agent computes the control that maximizes overall target position accuracy via a local estimate of the Fisher Information Matrix. We compared the proposed method with a state of the art algorithm outperforming it when the target movements do not follow a prescribed trajectory, with x100 less computation time, even when our method is running in one central CPU.