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

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.

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