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Journal Article Adaptive IIR/FIR fusion Filter and Its Application to the INS/GPS Integrated System
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Authors
Seong Yun Cho, Byung Doo Kim
Issue Date
2008-08
Citation
Automatica, v.44, no.8, pp.2040-2047
ISSN
0005-1098
Publisher
Elsevier
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1016/j.automatica.2007.11.009
Project Code
08MC2900, Development of Indoor/Outdoor Seamless Positioning Technology, Wan Sik Choi
Abstract
Motivated by the complementary features of the IIR-type filter and the FIR-type filter, this paper proposes a robust IIR/FIR fusion filter and an INS/GPS integrated system designed with the fusion filter. In the fusion filter, an IIR-type filter (SPKF) and a FIR-type filter (MRHKF filter) are processed independently, and then the two filters are merged using the mixing probability calculated using the residuals and residual covariance information of the two filters. The merits of the SPKF and the MRHKF filter are integrated and the demerits of the filters are diminished through the filter fusion. Consequently, the proposed fusion filter shows robustness against model uncertainty, temporary disturbing noise, large initial estimation error, etc. The stability of the fusion filter is verified by showing the closeness of two filters in the mixing/redistribution process and the upper bound of the error covariance matrices. This fusion filter is applied to an INS/GPS integrated system. The performance of the INS/GPS integrated system designed using the fusion filter is verified through a simulation under various error environments and is experimentally confirmed. © 2008 Elsevier Ltd. All rights reserved.
KSP Keywords
Covariance information, Filter fusion, INS/GPS integrated system, Model uncertainty, Upper bounds, error covariance matrices, estimation error