Distributed Fault Detection and Isolation for Kalman Consensus Filter
Project Members
Kartavya Neema, Daniel DeLaurentis
Kartavya Neema, Daniel DeLaurentis
Abstract
This research deals with the problem of developing a distributed fault detection methodology for recently developed distributed estimation algorithm called Kalman Consensus Filter (KCF). We extended the residual covariance matching techniques, developed for detecting faults in centralized Kalman filters, and use it for distributed fault detection in KCF. Faults present due to faulty sensor measurements are diagnosed and isolated from the system. Specifically, faults due to change in sensor noise statistics and outliers in the sensor measurements are considered. We further develop a Robust Kalman Consensus Filter algorithm and demonstrate the effectiveness of the algorithm using simulation results.