Forward-backward LMS adaptive line enhancer

Lim, Y.C.; Ko, Chi Chung

doi:10.1109/31.55068

Cited by 19 publications

(2 citation statements)

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“…To further improve the prediction performance, two-sided prediction scheme, forward-backward linear prediction (FBLP), which predicts the current sample by integrating symmetric past and future samples, is taken into consideration. Besides, it is shown in the studies by Hsue and Yagle (1995) and Lim and Ko (1990) that this algorithm can reduce the misadjustment error incurred by adaptive implementation.…”

Section: Introductionmentioning

confidence: 93%

“…Here, it is necessary to point out that, different from the common practice of symmetric coefficients in twosided prediction mode w -i (n) = w i (n) (Lim and Ko, 1990), we allow respective coefficients for symmetric sample w -i (n) = w i (n) (Chan et al, 2008), and therefore, an even better modeling is anticipated. The general form of tap weight vector update equation can be given by:…”

Section: Forward-backward Linear Prediction Algorithmmentioning

confidence: 99%

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A hybrid method for MEMS gyroscope signal error compensation

Guo

Sun

Wang

et al. 2018

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Purpose This paper aims to propose a hybrid method based on polynomial fitting bias self-compensation, grey forward-backward linear prediction (GFBLP) and moving average filter (MAF) for error compensation in micro-electromechanical system gyroscope signal especially under motion state. Design/methodology/approach The error compensation can be divided into two processes: bias correction and noise reduction. A polynomial drift angle fitting algorithm is used to correct bias before denoising processing. For noise reduction, operation can be taken in two stages: detection and processing. First, sample variances are used to judge motion state. According to the detection results, algorithmic system switches between grey GFBLP and MAF to ensure fast convergence rate and small steady-state error. Findings Experimental results show that the proposed method can correct bias effectively for practical gyroscope signal, and can eliminate noise effectively for both practical gyroscope signal and synthetic signal, which indicates the effectiveness of the proposed method. Originality/value Bias correction and noise reduction are considerations. Noise contained in practical or synthetic signal can be reduced rapidly and effectively, which benefits from the new idea of combination grey GFBLP, MAF and sample variances. And most importantly, it is applicable for signal denoising under arbitrary motion state condition, which is different from other methods where the convergence performance is seldom analyzed.

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Section: Introductionmentioning

confidence: 93%

Section: Forward-backward Linear Prediction Algorithmmentioning

confidence: 99%