DeepAI AI Chat
Log In Sign Up

Distributed Estimation Recovery under Sensor Failure

by   Mohammadreza Doostmohammadian, et al.
Sharif Accelerator
Tufts University
European Union

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 α and β 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.


page 1

page 2

page 3

page 4


Observational Equivalence in System Estimation: Contractions in Complex Networks

Observability of complex systems/networks is the focus of this paper, wh...

Structural cost-optimal design of sensor networks for distributed estimation

In this letter we discuss cost optimization of sensor networks monitorin...

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

This paper proposed a distributed filter for spatially interconnected sy...

Optimal Distributed Fault-Tolerant Sensor Fusion: Fundamental Limits and Efficient Algorithms

Distributed estimation is a fundamental problem in signal processing whi...

Linear TDOA-based Measurements for Distributed Estimation and Localized Tracking

We propose a linear time-difference-of-arrival (TDOA) measurement model ...

Consensus-based Distributed Quantile Estimation in Sensor Networks

A quantile is defined as a value below which random draws from a given d...