# Distributed Kalman filtering with minimum-time consensus algorithm

arXiv: https://arxiv.org/abs/1703.05438 · HTML: https://mlsanity.com/paper/1703.05438/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2017.

## Built on by (most cited first, 1 of 1)

- [Edge-Aided Sensor Data Sharing in Vehicular Communication Networks](https://mlsanity.com/paper/2206.08882/) (2022)

## Abstract (arXiv)

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.