Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20253
Title: Model-set adaptation using a fuzzy Kalman filter
Authors: Ding, Z
Leung, H
Chan, K 
Zhu, Z
Keywords: Adaptive IMM algorithm
Fuzzy Kalman filter
IMM algorithm
Model-set adaptation
Target tracking
Issue Date: 2001
Publisher: Pergamon Press
Source: Mathematical and computer modelling, 2001, v. 34, no. 7-8, p. 799-812 How to cite?
Journal: Mathematical and computer modelling 
Abstract: In this paper, a fuzzy Kalman filter (KF) is proposed to combat the model-set adaptation problem of multiple model estimation. The fuzzy KF is found to be able to more exactly extract dynamic information of target maneuvers. It uses a set of fuzzy rules to adaptively control the process noise covariance of the KF and that makes it more suitable for real radar tracking. The proposed fuzzy Kalman filter is then incorporated into an interacting multiple model (IMM) algorithm, hence, a fuzzy IMM (FIMM) algorithm is obtained. The performance of the FIMM algorithm is compared with that of an adaptive IMM (AIMM) algorithm using real radar data. Simulation result shows that the FIMM algorithm greatly outperforms the AIMM algorithm in terms of both the root mean square prediction error and the number of track loss.
URI: http://hdl.handle.net/10397/20253
ISSN: 0895-7177
DOI: 10.1016/S0895-7177(01)00100-5
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