Distributed Recursive Filtering for Spatially Interconnected Systems with Randomly Occurred Missing Measurements

TL;DR

This paper introduces a distributed recursive filtering method for spatially interconnected systems that effectively handles missing sensor measurements with known probabilities, ensuring accurate state estimation despite sensor failures.

Contribution

It proposes a novel distributed filtering approach for SISs that accounts for randomly missing measurements, enhancing robustness in sensor network applications.

Findings

Filtering method accurately estimates states with missing data

Experimental results validate effectiveness under sensor failure scenarios

Approach maintains performance with probabilistic measurement losses

Abstract

This paper proposed a distributed filter for spatially interconnected systems (SISs), which considers missing measurements in the sensors of sub-systems. An SIS is established by many similar sub-systems that directly interact or communicate with connective neighbors. Despite that the interactions are simple and tractable, the overall SIS can perform rich and complex behaviors. In actual projects, sensors of sub-systems in a sensor network may break down sometimes, which causes parts of the measurements unavailable unexpectedly. In this work, distributed characteristics of SISs are described by Andrea model and the losses of measurements are assumed to occur with known probabilities. Experimental results confirm that, this filtering method can be effectively employed for the state estimation of SISs, when missing measurements occur.