Distributed Kalman filtering with minimum-time consensus algorithm

TL;DR

This paper introduces a novel distributed Kalman filtering method that accelerates convergence to centralized estimates by integrating local covariance computation and minimal-time consensus algorithms, validated through theory and simulations.

Contribution

It presents a new distributed Kalman filter approach with a local covariance scheme and minimal-time consensus, improving convergence speed over existing methods.

Findings

Accelerates convergence to centralized Kalman filter estimates.

Enables distributed computation of measurement noise covariance.

Validated through theoretical analysis and extensive simulations.

Abstract

Fueled by applications in sensor networks, these years have witnessed a surge of interest in distributed estimation and filtering. A new approach is hereby proposed for the Distributed Kalman Filter (DKF) by integrating a local covariance computation scheme. Compared to existing well-established DKF methods, the virtue of the present approach lies in accelerating the convergence of the state estimates to those of the Centralized Kalman Filter (CKF). Meanwhile, an algorithm is proposed that allows each node to compute the averaged measurement noise covariance matrix within a minimal discrete-time running steps in a distributed way. Both theoretical analysis and extensive numerical simulations are conducted to show the feasibility and superiority of the proposed method.