A new distributed Kalman filtering based on means-quare estimation upper bounds

Title: A new distributed Kalman filtering based on means-quare estimation upper bounds
Creator: Li, Zhipeng; Fu, Minyue; Zhang, Huanshui
Relation: Journal of the Franklin Institute Vol. 355, Issue 9, p. 4032-4049
Publisher Link: http://dx.doi.org/10.1016/j.jfranklin.2018.01.044
Publisher: Elsevier
Resource Type: journal article
Date: 2018
Description: In this paper, a novel distributed Kalman filter consisting of a bank of interlaced filters is proposed for a signal model whose dynamic equation and measurement equation are coupled. Each of the interlaced filters estimates a part of state rather than the global state using its and its neighbor information, which is different from other distributed filters already existed (e.g., distributed Kalman filter based on diffusion strategy or consensus strategy, distributed fuzzy filter and distributed particle filter with Gaussian mixer approximation, etc). This relieves the calculation and communication burden in networks. In addition, the proposed distributed Kalman filtering contains no consensus strategies, which is useful in some cases since consensus usually requires an infinite number of iterations.
Identifier: http://hdl.handle.net/1959.13/1420919
Identifier: uon:37654
Identifier: ISSN:0016-0032
Language: eng
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