✦ LIBER ✦

Adaptive fading Kalman filter with an application

✍ Scribed by Qijun Xia; Ming Rao; Yiqun Ying; Xuemin Shen

Publisher: Elsevier Science
Year: 1994
Tongue: English
Weight: 487 KB
Volume: 30
Category: Article
ISSN: 0005-1098
DOI: 10.1016/0005-1098(94)90112-0

No coin nor oath required. For personal study only.

✦ Synopsis

A new adaptive state estimation algorithm, namely adaptive fading Kalman filter (AFKF), is proposed to solve the divergence problem of Kalman filter. A criterion function is constructed to measure the optimality of Kalman filter. The forgetting factor in AFKF is adaptively adjusted by minimizing the defined criterion function using measured outputs. The algorithm remains convergent and tends to be optimal in the presence of model errors. It has been successfully applied to the headbox of a paper-making machine for state estimation.

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