センサネットワークにおける位置推定と目標追跡の同時推定
Joint Estimation and Localization in Sensor Networks
無線センサネットワークで動的目標を追跡する際、センサ自身の位置もノイズを含む相対計測から分散的に推定し、目標追跡と位置推定を同時に行う手法を提案し、収束性を証明した論文。
著者: Nikolay A. Atanasov, Roberto Tron, Victor M. Preciado, George J. Pappas
分類: cs.MA, cs.NI, cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper addresses the problem of collaborative tracking of dynamic targets in wireless sensor networks. A novel distributed linear estimator, which is a version of a distributed Kalman filter, is derived. We prove that the filter is mean square consistent in the case of static target estimation. When large sensor networks are deployed, it is common that the sensors do not have good knowledge of their locations, which affects the target estimation procedure. Unlike most existing approaches for target tracking, we investigate the performance of our filter when the sensor poses need to be estimated by an auxiliary localization procedure. The sensors are localized via a distributed Jacobi algorithm from noisy relative measurements. We prove strong convergence guarantees for the localization method and in turn for the joint localization and target estimation approach. The performance of our algorithms is demonstrated in simulation on environmental monitoring and target tracking tasks.
