Ultimate iterative UFIR filtering algorithm

Shmaliy, Yuriy S.; Khan, Sharief; Zhao, Shunyi

doi:10.1016/j.measurement.2016.06.029

Cited by 32 publications

(17 citation statements)

References 53 publications

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“…The UFIR filter [3] can be more suitable for EMS signals, because it does not require any information about noise, except for the zero mean assumption [36,37]. To provide a near optimal estimate, this filter requires an averaging horizon [m, n] of N points, from m = n − N + 1 to n, to be optimal N opt in the MSE sense.…”

Section: Ufir Filtering Algorithmmentioning

confidence: 99%

Analysis and Smoothing of EMG Signal Envelope Using Kalman and UFIR Filtering under Colored Measurement Noise

2019

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This article describes some filtering methods to remove artifacts from the EMG signal envelope. Diverse EMG waveforms are studied using the Kalman filter (KF) and unbiased finite impulse response (UFIR) filter. The filters are developed in discrete-time state-space for Gauss-Markov colored measurement noise (CMN) and termed as cKF and cUFIR. It is shown that a choice of a proper CMN factor allows extracting the EMG waveform envelope with a high robustness. Extensive investigation have shown that the cKF and cUFIR filter are most efficient when the density is low of the motor unit action potential (MUAP) of the EMG and the Hilbert transform is required. Otherwise, when the envelope is well-pronounced and well-shaped with sharp edges due to a high MUAP density, the filters can be applied directly without using the Hilbert transform.

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Section: Ufir Filtering Algorithmmentioning

confidence: 99%

Analysis and Smoothing of EMG Signal Envelope Using Kalman and UFIR Filtering under Colored Measurement Noise

2019

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“…Using finite measurements ( ) on the most recent window [ − , ], FMS smoothers [6][7][8][9][10] as well as FMS filters [12][13][14][15] have been developed.…”

Section: Finite Memory Structure Estimation For State-space Modelmentioning

confidence: 99%

“…This FMS smoother has been known to have some good properties such as unbiasedness and deadbeat, which cannot be obtained by the IMS smoother. Moreover, in contrast to the IMS smoother with the recursive structure that tends to accumulate the smoothing error with the progression of time, the FMS smoother is inherently bounded input/bounded output stable and more robust against temporarily uncertain model parameters and round-off errors due to the FMS as shown in [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Finite Memory Structure Smoother with Recursive Form Using Forgetting Factor

Kim

2017

Mathematical Problems in Engineering

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This paper proposes an alternative finite memory structure (FMS) smoother with a recursive form under a least squares criterion using a forgetting factor strategy. The proposed FMS smoother does not require information of the noise covariances as well as the initial state. The proposed FMS smoother is shown to have good inherent properties such as time-invariance, unbiasedness, and deadbeat. The forgetting factor is shown to be considered as useful parameter to make the estimation performance of the proposed FMS smoother as good as possible. Through computer simulations for the F-404 engine system, it is shown that the proposed FMS smoother can be better than the existing FMS smoother for incorrect noise covariances and the IMS smoother for temporary uncertainties.

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“…In [14], the authors have presented an algorithm based on the KF to address an issue with missing data. Let us notice that the KF optimality has important requirements such as a complete knowledge of the noise statistics, the noise distribution must be strictly Gaussian, an adequate model must be used, and a knowledge of the initial conditions is mandatory [15][16][17][18]. If these requirements are not met, the performance of the KF may drastically degrade and become unacceptable for real world WSN applications [19].…”

Section: Introductionmentioning

confidence: 99%

Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs

2019

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Wireless sensor networks (WSN) are often deployed in harsh environments, where electromagnetic interference, damaged sensors, or the landscape itself cause the network to suffer from faulty links and missing data. In this paper, we develop an unbiased finite impulse response (UFIR) filtering algorithm for optimal consensus on estimates in distributed WSN. Simulations are provided assuming two possible scenarios with missing data. The results show that the distributed UFIR filter is more robust than the distributed Kalman filter against missing data.

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Ultimate iterative UFIR filtering algorithm

Cited by 32 publications

References 53 publications

Analysis and Smoothing of EMG Signal Envelope Using Kalman and UFIR Filtering under Colored Measurement Noise

Analysis and Smoothing of EMG Signal Envelope Using Kalman and UFIR Filtering under Colored Measurement Noise

A Finite Memory Structure Smoother with Recursive Form Using Forgetting Factor

Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs

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