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The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise
Author(s) -
Wang Xuehai,
Zhu Fang,
Ding Feng
Publication year - 2020
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3148
Subject(s) - kalman filter , noise (video) , extended kalman filter , wiener filter , control theory (sociology) , invariant extended kalman filter , recursive least squares filter , nonlinear system , computer science , algorithm , fast kalman filter , ensemble kalman filter , noise measurement , process (computing) , filter (signal processing) , mathematics , adaptive filter , noise reduction , artificial intelligence , computer vision , physics , control (management) , quantum mechanics , image (mathematics) , operating system
Summary This article develops the modified extended Kalman filter based recursive estimation algorithms for Wiener nonlinear systems with process noise and measurement noise. The prior estimate of the linear block output is computed based on the auxiliary model, and the posterior estimate is updated by designing a modified extended Kalman filter. A multi‐innovation gradient algorithm and a recursive least squares algorithm are derived to estimate the parameters of the linear subsystem, respectively. The simulation examples are provided to demonstrate the effectiveness of the proposed algorithms.