Horizon group shift FIR filter: Alternative nonlinear filter using finite recent measurements

Pak, Jung Min; Ahn, Choon Ki; Lim, Myo Taeg; Song, Moon Kyou

doi:10.1016/j.measurement.2014.07.007

Cited by 54 publications

(32 citation statements)

References 32 publications

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“…Although any of nonlinear FIR filters mentioned in Section 1 can be used in the WAEFFB, we select the extended MVF filter (EMVFF) [20] based on the general MVF filter. Now, we introduce the EMVFF.…”

Section: Extended Fir Filtermentioning

confidence: 99%

“…This error accumulation often leads to performance degradation or divergence of the KF. In order to overcome this problem, finite impulse response (FIR) filters [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] were developed. Since FIR filters use recent finite measurements to generate state estimates, they can prevent error accumulation and have built-in bounded-input bounded-output (BIBO) stability.…”

Section: Introductionmentioning

confidence: 99%

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Weighted average extended FIR filter bank to manage the horizon size in nonlinear FIR filtering

Pak¹,

Yoo²,

Lim³

et al. 2014

Int. J. Control Autom. Syst.

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In this paper, we propose a novel approach to manage the horizon size in nonlinear finite impulse response (FIR) filtering. The proposed approach is to perform state estimation through a bank of FIR filters called a weighted average extended FIR filter bank (WAEFFB). In the WAEFFB, the state estimate is obtained by weighting the average of multiple estimates from a bank of extended FIR filters that uses different horizon sizes. The horizon sizes used for the WAEFFB are adjusted constantly by maximizing the likelihood function. We show through simulations that the WAEFFB yields better results than the conventional approach that uses a constant (i.e., fixed) horizon size.

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Section: Extended Fir Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weighted average extended FIR filter bank to manage the horizon size in nonlinear FIR filtering

Pak¹,

Yoo²,

Lim³

et al. 2014

Int. J. Control Autom. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Because the horizon size, usually denoted N, is a critical problem in FIR filtering [20,22,23]. Various approaches to finding an optimal N (denoted N opt ) for general FIR filters has been proposed based on the minimum mean square value [16], bank of FIR filters [20], and Monte Carlo simulation [14,15], but an analytic method to calculate N opt has not yet been found.…”

Section: Introductionmentioning

confidence: 99%

Optimal horizon size for unbiased finite memory digital phase-locked loop

You

Pak

Kim

2017

IEICE Electron. Express

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Digital phase locked-loop (DPLL) is a circuit system for frequency synchronization, and unbiased finite memory DPLL (UFMDPLL) is DPLL using a finite impulse response (FIR) filter for phase detection. This letter proposes a novel method for finding the optimal horizon size, which is a key design parameter of UFMDPLL, based on the minimization of the estimation error variance. The effectiveness and efficiency of the proposed method are demonstrated in comparisons using the conventional Monte Carlo simulation method.

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“…Nowadays, the interest to FIR estimators has grown owing to the tremendous progress in the computational resources. Accordingly, we find a number of new solutions on FIR filtering [16][17][18][19][20][21], smoothing [22][23][24], and prediction [25][26][27] as well as efficient applications [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Effect of embedded unbiasedness on discrete-time optimal FIR filtering estimates

Zhao

Shmaliy

Liu

et al. 2015

EURASIP J. Adv. Signal Process.

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Unbiased estimation is an efficient alternative to optimal estimation when the noise statistics are not fully known and/or the model undergoes temporary uncertainties. In this paper, we investigate the effect of embedded unbiasedness (EU) on optimal finite impulse response (OFIR) filtering estimates of linear discrete time-invariant statespace models. A new OFIR-EU filter is derived by minimizing the mean square error (MSE) subject to the unbiasedness constraint. We show that the OFIR-UE filter is equivalent to the minimum variance unbiased FIR (UFIR) filter. Unlike the OFIR filter, the OFIR-EU filter does not require the initial conditions. In terms of accuracy, the OFIR-EU filter occupies an intermediate place between the UFIR and OFIR filters. Contrary to the UFIR filter which MSE is minimized by the optimal horizon of N opt points, the MSEs in the OFIR-EU and OFIR filters diminish with N and these filters are thus full-horizon. Based upon several examples, we show that the OFIR-UE filter has higher immunity against errors in the noise statistics and better robustness against temporary model uncertainties than the OFIR and Kalman filters.

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Horizon group shift FIR filter: Alternative nonlinear filter using finite recent measurements

Cited by 54 publications

References 32 publications

Weighted average extended FIR filter bank to manage the horizon size in nonlinear FIR filtering

Weighted average extended FIR filter bank to manage the horizon size in nonlinear FIR filtering

Optimal horizon size for unbiased finite memory digital phase-locked loop

Effect of embedded unbiasedness on discrete-time optimal FIR filtering estimates

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