Blind Robust Estimation With Missing Data for Smart Sensors Using UFIR Filtering

Vazquez-Olguin, Miguel; Shmaliy, Yuriy S.; Ahn, Choon Ki; Ibarra-Manzano, Oscar

doi:10.1109/jsen.2017.2654306

Cited by 24 publications

(6 citation statements)

References 48 publications

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“…Of importance is that the UFIR estimate (2b) does not require the noise statistics and initial values. The zero mean noise v n is allowed to have any distribution and covariance [53, 54] that is a fundamental difference with optimal estimates.…”

Section: Methodsmentioning

confidence: 99%

ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing

Lastre-Dominguez

Shmaliy

Ibarra-Manzano

et al. 2019

BioMed Research International

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Methods of the electrocardiography (ECG) signal features extraction are required to detect heart abnormalities and different kinds of diseases. However, different artefacts and measurement noise often hinder providing accurate features extraction. One of the standard techniques developed for ECG signals employs linear prediction. Referring to the fact that prediction is not required for ECG signal processing, smoothing can be more efficient. In this paper, we employ the p-shift unbiased finite impulse response (UFIR) filter, which becomes smooth by p<0. We develop this filter to have an adaptive averaging horizon: optimal for slow ECG behaviours and minimal for fast excursions. It is shown that the adaptive UFIR algorithm developed in such a way provides better denoising and suboptimal features extraction in terms of the output signal-noise ratio (SNR). The algorithm is developed to detect durations and amplitudes of the P-wave, QRS-complex, and T-wave in the standard ECG signal map. Better performance of the algorithm designed is demonstrated in a comparison with the standard linear predictor, UFIR filter, and UFIR predictive filter based on real ECG data associated with normal heartbeats.

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Section: Methodsmentioning

confidence: 99%

ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing

Lastre-Dominguez

Shmaliy

Ibarra-Manzano

et al. 2019

BioMed Research International

Self Cite

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“…The disadvantage of this method is the lack of proof of its convergence. This paper deals with finite impulse response (FIR) filters for state estimation of linear discrete systems which are extensively employed in a variety of applications see for instance [30][31][32][33][34][35][36][37][38][39][40]. Unlike the KF, they allow to avoid the divergence and unsatisfactory object tracking connected with temporary perturbations, errors in the noise statistics setting, abrupt object changes [1,14,15].…”

Section: Introductionmentioning

confidence: 99%

Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

Skorohod

2023

Journal of Robotics and Control

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It is known, that unlike the Kalman filter (KF) finite impulse response (FIR) filters allow to avoid the divergence and unsatisfactory object tracking connected with temporary perturbations and abrupt object changes. The main challenge is to provide the appropriate choice of a sliding window size for them. In this paper, the new finite impulse response (FIR) filtering algorithm with the adaptive horizon size selection is proposed. The algorithm uses the receding horizon optimal (RHOFIR) filter which receives estimates, an abrupt change detector and an adaptive recurrent mechanism for choosing the window size. Monotonicity and asymptotic properties of the estimation error covariance matrix and the RHOFIR filter gain are established. These results form a solid foundation for justifying the principal possibility to tune the filter gain using them and the developed adaptation mechanism. The proposed algorithm (the ARHOFIR filter) allows reducing the impact of disturbances by varying adaptively the sliding window size. The possibility of this follows from the fact that the window size affects the filter characteristics in different ways. The ARHOFIR filter chooses a large horizon size in the absence of abrupt disturbances and a little during the time intervals of their action. Due to this, it has better transient characteristics compared to the KF and RHOFIR filter at intervals where there is temporary uncertainty and may provide the same accuracy of estimates as the KF in their absence. By simulation, it is shown that the ARHOFIR filter is more robust than the KF and RHOFIR filter for the temporarily uncertain systems.

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“…To improve the robustness in uncertain environments, an adaptive unbiased finite impulse response (UFIR) filter was designed in [36] and another adaptive UFIR solution was proposed for the INS/UWB integrated localization system in [37]. The outage problem was solved in [38] by predicting lost data using the prior filtering estimate. The predictive algorithm was also incorporated into the KF and UFIR filter, but has still not been developed for fusing structures required by integrated localization schemes.…”

Section: Introductionmentioning

confidence: 99%

Decision tree-extended finite impulse response filtering for pedestrian tracking over tightly integrated inertial navigation system/ultra wide band data

Xu¹,

Shmaliy²,

Hua³

et al. 2020

Meas. Sci. Technol.

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Although the tightly integrated inertial navigation system/ultra wide band (INS/UWB) improves the localization accuracy, it suffers from UWB distance outage. In order to reduce the outage effect on the position accuracy, in this paper we propose using a novel decision tree (DT)-extended finite impulse response (EFIR) filter. When all the UWB distances are available, the EFIR filter tightly fuses the INS and UWB data. Otherwise, the DT builds a relationship between the INS and UWB position errors. Once at least one UWB distance is unavailable, the DT bridges a gap over unavailable measurements. It is shown experimentally that the DT-EFIR filter is an efficient tool to reduce the effect of the UWB distance outage on the INS/UWB system operation, which can improve the localization error by about 40% as compared with the UWB solution.

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Blind Robust Estimation With Missing Data for Smart Sensors Using UFIR Filtering

Cited by 24 publications

References 48 publications

ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing

ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing

Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

Decision tree-extended finite impulse response filtering for pedestrian tracking over tightly integrated inertial navigation system/ultra wide band data

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