Unbiased Kalman Consensus Algorithm

Distributed Kalman filtering with minimum-time consensus algorithm

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 es...

The Extended Kalman-consensus Filter

Filtering of Discrete-Time State-Space models with the p-Shift Kalman-like Unbiased FIR Algorithm

In this paper, we show a simple way to derive the p-shift finite impulse response (FIR) unbiased estimator (UE) recently proposed by Shmaliy for time-invariant discrete-time state-space models. We also examine its iterative Kalman-like form. We conclude that the Kalman-like algorithm can serve efficiently as an optimal estimator with large averaging horizons. It has better engineering features ...

Asymptotically Unbiased Average Consensus under Measurement Noises and Fixed Topologies

This paper is concerned with average-consensus control under directed topologies and random measurement noises. To attenuate the measurement noises, time-varying consensus gains are introduced in the protocol. It is shown that under the protocol designed, all agents’ states converge to a common Gaussian random variable, whose mathematical expectation is just the average of the initial states, a...

Is Your Permutation Algorithm Unbiased for n

Many papers on parallel random permutation algorithms assume the input size n to be a power of two and imply that these algorithms can be easily generalized to arbitrary n. We show that this simplifying assumption is not necessarily correct since it may result in a bias. Many of these algorithms are, however, consistent, i.e., iterating them ultimately converges against an unbiased permutation....