Decentralized Input and State Estimation for Multi-agent System with Dynamic Topology and Heterogeneous Sensor Network

TL;DR

This paper introduces a decentralized, privacy-preserving state estimation algorithm for multi-agent systems with dynamic topologies and heterogeneous sensors, achieving optimal accuracy with minimal communication.

Contribution

It presents a novel single-iteration consensus algorithm using information filter decomposition and covariance intersection, improving efficiency and robustness in dynamic, heterogeneous networks.

Findings

Performs comparably or better than algorithms with full network information.

Requires only one communication iteration for estimate exchange.

Enhances robustness against intermittent observations and incomplete data.

Abstract

A crucial challenge in decentralized systems is state estimation in the presence of unknown inputs, particularly within heterogeneous sensor networks with dynamic topologies. While numerous consensus algorithms have been introduced, they often require extensive information exchange or multiple communication iterations to ensure estimation accuracy. This paper proposes an efficient algorithm that achieves an unbiased and optimal solution comparable to filters with full information about other agents. This is accomplished through the use of information filter decomposition and the fusion of inputs via covariance intersection. Our method requires only a single communication iteration for exchanging individual estimates between agents, instead of multiple rounds of information exchange, thus preserving agents' privacy by avoiding the sharing of explicit observations and system equations. Furthermore, to address the challenges posed by dynamic communication topologies, we propose two practical strategies to handle issues arising from intermittent observations and incomplete state estimation, thereby enhancing the robustness and accuracy of the estimation process. Experiments and ablation studies conducted in both stationary and dynamic environments demonstrate the superiority of our algorithm over other baselines. Notably, it performs as well as, or even better than, algorithms that have a global view of all neighbors.