Mission-Aware Value of Information Censoring for Distributed Filtering

TL;DR

This paper introduces a mission-aware, communication-efficient distributed filtering algorithm that uses VoI censoring and ADMM to optimize information sharing among agents, demonstrated through target tracking simulations.

Contribution

It proposes a novel mission-aware VoI censoring mechanism integrated with ADMM for distributed filtering, enhancing communication efficiency while maintaining estimation accuracy.

Findings

Reduced communication load via VoI censoring

Maintained estimation accuracy with mission-specific requirements

Effective in target tracking simulation scenarios

Abstract

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.