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
An adaptive filter with adaptation to nonuniformly spaced samples
β Scribed by Miwa Sakai; Kiyoharu Aizawa; Mitsutoshi Hatori
- Publisher
- John Wiley and Sons
- Year
- 1996
- Tongue
- English
- Weight
- 700 KB
- Volume
- 79
- Category
- Article
- ISSN
- 1042-0967
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β¦ Synopsis
A novel adaptive filter is proposed that adapts to the sample spacing as well IIS to the tap weights. Although in adaptive digital procesSing, the tappeddelay line usualIy has uniformly spaced taps, with the spacing equal to symbol period or a fraction of symbol period, an adaptive filter with a few taps should be adaptively arranged for nonuniform spacing for better convergence as the sampling timing varies. The conventional nonuniformly spaced filters are not adaptively changed or not m g e d under the constraint of the pcceptable meansquare error. In this paper, a least-mean square (LMS)type algorithm is derived in which an adaptive filter adaptively changes the amount of delay, as well as the filter coefficients. The performance of the proposed algorithm is investigated by computer simulation.
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