分散フィルタリングにおけるミッション認識型情報価値検閲
Mission-Aware Value of Information Censoring for Distributed Filtering
ネットワーク内の各エージェントが近傍とのみ通信しながら分散推定を行う際、情報価値に基づいて通信を検閲することで通信効率を高める手法を提案し、目標追跡シミュレーションで有効性を示した。
著者: Miguel Calvo-Fullana, Jonathan P. How
分類: cs.RO, math.OC
原文アブストラクト
In this paper, we study the problem of distributed estimation with an emphasis on communication-efficiency. The proposed algorithm is based on a windowed maximum a posteriori (MAP) estimation problem, wherein each agent in the network locally computes a Kalman-like filter estimate that approximates the centralized MAP solution. Information sharing among agents is restricted to their neighbors only, with guarantees on overall estimate consistency provided via logarithmic opinion pooling. The problem is efficiently distributed using the alternating direction method of multipliers (ADMM), whose overall communication usage is further reduced by a value of information (VoI) censoring mechanism, wherein agents only transmit their primal-dual iterates when deemed valuable to do so. The proposed censoring mechanism is mission-aware, enabling a globally efficient use of communication resources while guaranteeing possibly different local estimation requirements. To illustrate the validity of the approach we perform simulations in a target tracking scenario.
