Unbiased FIR Filtering for Time-Stamped Discretely Delayed and Missing Data

Uribe-Murcia, Karen; Shmaliy, Yuriy S.; Ahn, Choon Ki; Zhao, Shunyi

doi:10.1109/tac.2019.2937850

Cited by 28 publications

(16 citation statements)

References 31 publications

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“…The estimate and error covariance can then be updated by developing the alternative KF recursions originally derived in [27] and modified in [28]. Accordingly, we have…”

Section: Kalman Filtering Algorithmmentioning

confidence: 99%

“…It worth noting that, by θ (i) = 0, the H ∞ filter becomes the KF. However, care must be taken to set θ (i) properly to avoid the divergence [28], [29].…”

Section: Recursive H∞ Filtering Algorithmmentioning

confidence: 99%

“…Since ripples in the MUAP burst are bounded by saturation, the game theory H ∞ filter significantly reduces errors by a properly set scalar correction factor θ [27]. However, this filter is highly sensitive to θ and prone to divergence [28]. The unbiased finite impulse response (UFIR) filter [29] is another opportunity to extract a smooth envelope.…”

Section: Introductionmentioning

confidence: 99%

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Improving Myoelectric Prosthetic Arm Proportional Control Under MUAP Envelope Colored Noise

2021

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Accuracy and stability of the myoelectric prosthetic arm digital proportional control strongly depends on shape of the rectified motor unit action potential (MUAP) envelope. To mitigate effects caused by the envelope-induced torque ripples, mechanical vibrations (TRMV), and electromechanical delays (EMD), in this paper we develop in discrete-time state-space the unbiased finite impulse response (UFIR) filter, Kalman filter (KF), and game theory recursive H ∞ filter. The filters developed are called the cUFIR filter, cKF, and cH ∞ filter and applied to extract a smooth near Gaussian MUAP envelope by viewing ripples as colored measurement noise (CMN). Additional UFIR and Rauch-Tung-Striebel (RTS) smoothers are also used to improve the performance when the EMD is acceptable. Based on experimental examples of prosthetic arm control, it is shown that the advanced cUFIR filter, cKF, and cH ∞ filter improve the performance of the original filters by the factor of about 10 and essentially reduce the TRMV and EMD.INDEX TERMS Electromyography, MUAP envelope, filtering, smoothing, prosthetic arm control.

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“…The estimate and error covariance can then be updated by developing the alternative KF recursions originally derived in [27] and modified in [28]. Accordingly, we have…”

Section: Kalman Filtering Algorithmmentioning

confidence: 99%

“…It worth noting that, by θ (i) = 0, the H ∞ filter becomes the KF. However, care must be taken to set θ (i) properly to avoid the divergence [28], [29].…”

Section: Recursive H∞ Filtering Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving Myoelectric Prosthetic Arm Proportional Control Under MUAP Envelope Colored Noise

2021

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“…However, [18] restricts the delay up to one sampling interval, wherein the practical delays can often be larger. Furthermore, [33] introduced unbiased finite impulse response-based filtering approach for finite-horizon case, considering the presence of delayed and missing measurements. However, it assumes that the delay is time-stamped, while delays without timestamping are observed in many practical systems [35], [36].…”

Section: Introductionmentioning

confidence: 99%

Gaussian Filtering for Simultaneously Occurring Delayed and Missing Measurements

et al. 2022

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Approximate filtering algorithms in nonlinear systems assume Gaussian prior and predictive density and remain popular due to ease of implementation as well as acceptable performance. However, these algorithms are restricted by two major assumptions: they assume no missing or delayed measurements. However, practical measurements are frequently delayed and intermittently missing. In this paper, we introduce a new extension of the Gaussian filtering to handle the simultaneous occurrence of the delay in measurements and intermittently missing measurements. Our proposed algorithm uses a novel modified measurement model to incorporate the possibility of the delayed and intermittently missing measurements. Subsequently, it redesigns the traditional Gaussian filtering for the modified measurement model. Our algorithm is a generalized extension of the Gaussian filtering, which applies to any of the traditional Gaussian filters, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF). A further contribution of this paper is that we study the stochastic stability of the proposed method for its EKF-based formulation. We compared the performance of the proposed filtering method with the traditional Gaussian filtering (particularly the CKF) and three extensions of the traditional Gaussian filtering that are designed to handle the delayed and missing measurements individually or simultaneously.INDEX TERMS Delayed measurements, Gaussian filtering, missing measurements, nonlinear Bayesian filtering.

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“…The advantages of the UFIR filter meet industry requirements (63). To note, UFIR is the most robust among the FIR variants as stated in (61,64,65). UFIR filters have been effectively applied in numerous engineering applications, including applications in global positioning systems (GPS)-based vehicle tracking over a wireless sensor network (WSN) (66), an electrocardiogram (ECG) data for features extraction (67,68), and state estimation of carbon monoxide concentration (69)(70)(71).…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems

Rahman

Aziz

Ibrahim

et al. 2020

Preprint

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This paper investigates the potential of the ultimate iterative unbiased finite impulse response (UFIR) filter as a source of inspiration in a population-based metaheuristic algorithm. Here, a new algorithm inspired by the measurement and estimation procedures of the UFIR filter named the Multi-Agent Finite Impulse Response Optimizer (MAFIRO) for solving numerical optimization problems is proposed. MAFIRO works with a set of agents where each performs the measurement and estimation to find a solution. MAFIRO employs a random mutation of the best-so-far solution and the shrinking local neighborhood method to balance between the exploration and exploitation phases during the optimization process. Subsequently, the performance of MAFIRO is tested by solving the benchmark test suite of the IEEE Congress on Evolutionary Computation 2014. The benchmark is composed of 30 mathematical functions. The competency of MAFIRO is compared with the Particle Swarm Optimization algorithm, Genetic Algorithm, and Grey Wolf Optimizer. The results show that MAFIRO leads in 23 out of 30 functions and has the highest Friedman rank. MAFIRO performs significantly better than the other tested algorithms. Based on the findings, we show that the concept of the UFIR filter is a good inspiration for a population-based metaheuristic algorithm.

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Unbiased FIR Filtering for Time-Stamped Discretely Delayed and Missing Data

Cited by 28 publications

References 31 publications

Improving Myoelectric Prosthetic Arm Proportional Control Under MUAP Envelope Colored Noise

Improving Myoelectric Prosthetic Arm Proportional Control Under MUAP Envelope Colored Noise

Gaussian Filtering for Simultaneously Occurring Delayed and Missing Measurements

Multi-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems

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