Performance Analysis of Distributed Kalman Filtering With Partial Diffusion Over Noisy Network

Abstract

The performance of a partial diffusion Kalman filtering (PDKF) algorithm for the networks with noisy links is studied here. A closed-form expression for the steady-state mean square deviation is then derived and theoretically shown that when the links are noisy, the communication-performance tradeoff, reported for the PDKF algorithm, does not hold. Additionally, optimal selection of combination weights is investigated, and a combination rule along with an adaptive implementation is motivated. The results confirm the theoretical outcome.