情報価値に基づく通信抑制を用いた分散フィルタリング
Distributed Filtering with Value of Information Censoring
各エージェントが推定値の情報価値（VoI）を評価し、価値の高いものだけを共有することで通信量を大幅に削減しつつ分散ベイズ推定を行う手法を提案し、シミュレーションと実無線ネットワークで有効性を検証した。
著者: Miguel Calvo-Fullana, Jonathan P. How
分類: cs.RO, math.OC
原文アブストラクト
This work presents a distributed estimation algorithm that efficiently uses the available communication resources. The approach is based on Bayesian filtering that is distributed across a network by using the logarithmic opinion pool operator. Communication efficiency is achieved by having only agents with high Value of Information (VoI) share their estimates, and the algorithm provides a tunable trade-off between communication resources and estimation error. Under linear-Gaussian models the algorithm takes the form of a censored distributed Information filter, which guarantees the consistency of agent estimates. Importantly, consistent estimates are shown to play a crucial role in enabling the large reductions in communication usage provided by the VoI censoring approach. We verify the performance of the proposed method via complex simulations in a dynamic network topology and by experimental validation over a real ad-hoc wireless communication network. The results show the validity of using the proposed method to drastically reduce the communication costs of distributed estimation tasks.
