Distributed information consensus filters for simultaneous input and state estimation

Tools

Lu, Y. and Zhang, L and Mao, Xuerong (2013) Distributed information consensus filters for simultaneous input and state estimation. Circuits, Systems, and Signal Processing, 32 (2). pp. 877-888. ISSN 1531-5878 (https://doi.org/10.1007/s00034-012-9460-8)

[thumbnail of Lu-etal-CSASP-2013-Distributed-information-consensus-filters-for-simultaneous-input]
Preview
 Text. Filename: Lu_etal_CSASP_2013_Distributed_information_consensus_filters_for_simultaneous_input.pdf
Accepted Author Manuscript
Download (211kB)| Preview

Abstract

This paper describes the distributed information filtering where a set of sensor networks are required to simultaneously estimate input and state of a linear discrete-time system from collaborative manner. Our research purpose is to develop a consensus strategy in which sensor nodes communicate within the network through a sequence of Kalman iterations and data diffusion. A novel recursive information filtering is proposed by integrating input estimation error into measurement data and weighted information matrices. On the fusing process, local system state filtering transmits estimation information using the consensus averaging algorithm, which penalizes the disagreement in a dynamic manner. A simulation example is provided to compare the performance of the distributed information filtering with optimal Gillijins–De Moor’s algorithm.

ORCID iDs

Lu, Y., Zhang, L and Mao, Xuerong ORCID logoORCID: https://orcid.org/0000-0002-6768-9864;
CORE (COnnecting REpositories)