A receding horizon unbiased FIR filter for discrete-time state space models

Кwon, Wook Hyun; Kim, Pyung Soo; Han, Seung Baik

doi:10.1016/s0005-1098(01)00242-4

Cited by 158 publications

(81 citation statements)

References 6 publications

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“…It has been well acknowledged in signal processing areas and estimation theories that FIR structure tends to be more robust and has the faster response, and hence FIR filters have been more commonly employed compared with IIR filters [9,10,11]. In order to employ such good properties of FIR structure, the optimal FIR filter approach was suggested in [6].…”

Section: Introductionmentioning

confidence: 99%

Improved Receding Horizon Fourier Analysis for Quasi-periodic Signals

Kwon¹,

Han²,

Han³

2017

Journal of Electrical Engineering and Technology

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-In this paper, an efficient short-time Fourier analysis method for the quasi-periodic signals is proposed via an optimal fixed-lag finite impulse response (FIR) smoother approach using a receding horizon scheme. In order to deal with time-varying Fourier coefficients (FCs) of quasi-periodic signals, a state space model including FCs as state variables is augmented with the variants of FCs. Through an optimal fixed-lag FIR smoother, FCs and their increments are estimated simultaneously and combined to produce final estimates. A lag size of the optimal fixed-lag FIR smoother is chosen to minimize the estimation error. Since the proposed estimation scheme carries out the correction process with the estimated variants of FCs, it is highly probable that the smaller estimation error is achieved compared with existing approaches not making use of such a process. It is shown through numerical simulation that the proposed scheme has better tracking ability for estimating time-varying FCs compared with existing ones.

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Section: Introductionmentioning

confidence: 99%

Improved Receding Horizon Fourier Analysis for Quasi-periodic Signals

Kwon¹,

Han²,

Han³

2017

Journal of Electrical Engineering and Technology

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“…The valid duration of the model might as well be limited to the recently finite time, which is the basic theory of the finite memory structure (FMS) filters. [8][9][10][11][12][13] In addition, due to the IMS, the Kalman filter can tend to accumulate the filtering error as time goes. Thus, the Kalman filter has known to be sensitive and show even divergence phenomenon for temporary modeling uncertainties and round-off errors.…”

Section: Introductionmentioning

confidence: 99%

A finite memory structure filtering for indoor positioning in wireless sensor networks with measurement delay

Kim

Lee

Jang

et al. 2017

International Journal of Distributed Sensor Networks

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In this article, an alternative indoor positioning mechanism is proposed considering finite memory structure filter as well as measurement delay. First, a finite memory structure filter with a measurement delay is designed for the indoor positioning mechanism under a weighted least-squares criterion, which utilizes only finite measurements on the most recent window. The proposed finite memory structure filtering-based mechanism gives the filtered estimates for position, velocity, and acceleration of moving target in real time, while removing undesired noisy effects and preserving desired moving positions. Second, the proposed mechanism is shown to have good inherent properties such as unbiasedness, efficiency, time-invariance, deadbeat, and robustness due to the finite memory structure. Third, through discussions about the choice of window length, it is shown that this can be considered as a useful design parameter to make the performance of the proposed mechanism as good as possible. Finally, computer simulations show that the performance of the proposed finite memory structure filtering-based mechanism can outperform the existing infinite memory structure filtering-based mechanism for the abruptly varying acceleration of moving target. KeywordsIndoor positioning system, wireless sensor networks, measurement delay, finite memory structure filter, infinite memory structure filter.

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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%

“…In [16], the receding horizon FIR-CU filter was derived from KF with no requirements for the initial state. Soon after, a receding horizon FIR-EU filter was proposed by Kwon, Kim, and Han in [17], where the unbiasedness condition was considered as a constraint to the optimization problem. Later, the receding horizon FIR smoothers were found in [22] for CU by employing the maximum likelihood and in [24] for EU by minimizing the error variance.…”

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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A receding horizon unbiased FIR filter for discrete-time state space models

Cited by 158 publications

References 6 publications

Improved Receding Horizon Fourier Analysis for Quasi-periodic Signals

Improved Receding Horizon Fourier Analysis for Quasi-periodic Signals

A finite memory structure filtering for indoor positioning in wireless sensor networks with measurement delay

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

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