NDLI: A new adaptive Kalman filter by combining evolutionary algorithm and fuzzy inference system

Content Provider	IEEE Xplore Digital Library
Author	Yudan Huo Zhihua Cai Wenyin Gong Qin Liu
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., China Univ. of Geosci., Wuhan, China (Yudan Huo; Zhihua Cai; Wenyin Gong; Qin Liu)
Abstract	The performance of the Kalman filter (KF), which is recognized as an outstanding tool for dynamic system state estimation, heavily depends on its parameter R, called the measurement noise covariance matrix. However, it's difficult to get the exact value of R before the filter starts, and the value of R is likely to change with the measurement environment when the filter is working. To solve this problem, a new parameter adaptive Kalman filter is proposed in this paper. In this new Kalman filter, the initial value of R is offline decided by Evolutionary Algorithm (EA), and the value of R decided by EA is online updated by Fuzzy Inference System (FIS). A simulation experiment based on target tracking is carried out, and the results demonstrate that the new adaptive Kalman filter proposed in this paper (HydGeFuzKF) has a stronger adaptability to time-varying measurement noises than regular Kalman filter (RegularKF), Sage-Husa adaptive Kalman filter (SageHusaKF), the adaptive Kalman filter only based on genetic algorithm (GeneticKF) and the adaptive Kalman filter only based on fuzzy inference system (FuzzyKF).
Starting Page	2893
Ending Page	2900
File Size	671538
Page Count	8
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900320
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kalman filters Fuzzy logic Noise Covariance matrices Noise measurement Genetic algorithms Mathematical model
Content Type	Text
Resource Type	Article

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