Distributed Estimation Recovery under Sensor Failure

TL;DR

This paper proposes a polynomial order algorithm for recovering distributed estimation in sensor networks experiencing failures, enabling efficient observability recovery in large-scale systems with minimal consensus steps.

Contribution

It introduces a novel polynomial order algorithm for sensor failure recovery in distributed estimation, applicable to large-scale systems and addressing both nd eta-sensors.

Findings

Effective recovery of distributed observability after sensor failure.

Polynomial order algorithms are computationally efficient for large systems.

Enhanced robustness of distributed estimation in sensor networks.

Abstract

Single time-scale distributed estimation of dynamic systems via a network of sensors/estimators is addressed in this letter. In single time-scale distributed estimation, the two fusion steps, consensus and measurement exchange, are implemented only once, in contrast to, e.g., a large number of consensus iterations at every step of the system dynamics. We particularly discuss the problem of failure in the sensor/estimator network and how to recover for distributed estimation by adding new sensor measurements from equivalent states. We separately discuss the recovery for two types of sensors, namely \alpha and \beta sensors. We propose polynomial order algorithms to find equivalent state nodes in graph representation of system to recover for distributed observability. The polynomial order solution is particularly significant for large-scale systems.