Multi-sensor State Estimation over Lossy Channels using Coded Measurements

TL;DR

This paper introduces a coding scheme for multi-sensor state estimation over lossy channels that optimizes communication and guarantees estimator stability, with proven theoretical conditions and simulation validation.

Contribution

It proposes a novel measurement coding scheme that balances communication load and stability guarantees, improving robustness in networked state estimation.

Findings

The coding scheme is optimal within linear causal coders for given communication constraints.

A necessary and sufficient stability condition is derived for the MMSE estimator.

Simulations confirm the scheme's effectiveness and advantages.

Abstract

This paper focuses on a networked state estimation problem for a spatially large linear system with a distributed array of sensors, each of which offers partial state measurements, and the transmission is lossy. We propose a measurement coding scheme with two goals. Firstly, it permits adjusting the communication requirements by controlling the dimension of the vector transmitted by each sensor to the central estimator. Secondly, for a given communication requirement, the scheme is optimal, within the family of linear causal coders, in the sense that the weakest channel condition is required to guarantee the stability of the estimator. For this coding scheme, we derive the minimum mean-square error (MMSE) state estimator, and state a necessary and sufficient condition with a trivial gap, for its stability. We also derive a sufficient but easily verifiable stability condition, and quantify the advantage offered by the proposed coding scheme. Finally, simulations results are presented to confirm our claims.