ISEE.U: 予測不能なターゲットに対する分散オンライン能動的ターゲット位置推定
ISEE.U: Distributed online active target localization with unpredictable targets
各エージェントがノイズを含む距離情報からフィッシャー情報行列を局所推定し、分散・パラメータ不要でターゲット位置精度を最大化するオンライン能動学習アルゴリズムを提案。予測不能な動きに対しても頑健で、集中型CPU実行でも計算時間が100分の1と高速。
著者: Miguel Vasques, Claudia Soares, João Gomes
分類: cs.LG, cs.RO, cs.SY, eess.SY, math.OC
原文アブストラクト
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.
