Frequency estimation of distorted non-stationary signals using complex H∞ filter

Sahoo, Harish Kumar; Dash, P.K.; Rath, N. P.

doi:10.1016/j.aeue.2011.08.002

Cited by 12 publications

(8 citation statements)

References 25 publications

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“…In both LS1 and LS2 algorithms the first LS method and the simple comb filter (10) are used for dc component cancelation, but with different k and m. The algorithm LOB uses the second LS method and the simple comb filter (10), and LSS uses the third LS method and the toothless-comb filter (29). In addition to that, all algorithms are investigated having been armed with the band-pass comb-like filter (30), instead of the comb filters (10) and (29).…”

Section: Performance Evaluation Through Simulationmentioning

confidence: 99%

On LS-Based Power Frequency Estimation Algorithms

Kušljević¹

2013

IEEE Trans. Instrum. Meas.

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This paper deals with fundamental properties of the least-squares-based estimation algorithms of power frequency, such as immunity and insensitivity to harmonics and noise. This paper is motivated by a recent study that dealt with an optimal case of the distance between equidistantly spaced samples. This paper extends and generalizes that approach by decoupling of task of canceling dc component from the least-squares estimation task. Two different algorithms derived as particular cases of this generalized approach are investigated. In addition, besides the basic least-squares (LS) method, the Prony-based method previously formulated by Lobos and Rezmer has also been investigated. The task of estimation frequency from a pure sine signal without dc component presence is also considered. Finally, the third method is derived and its basic properties are shown.Index Terms-Comb filter, frequency measurement, leastsquares (LS) method, Prony-based method.

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Section: Performance Evaluation Through Simulationmentioning

confidence: 99%

On LS-Based Power Frequency Estimation Algorithms

Kušljević¹

2013

IEEE Trans. Instrum. Meas.

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“…In contrast to Kalman filter which requires exact and accurate system model as well as perfect knowledge of the noise statistics, the H 1 filter [21][22][23] requires no aprior information of the noise statistics but finite bounded energies. Unscented H 1 filter (UHF) [27,28] is based on state-space representation of noisy sinusoidal signal and passing the sigma points through unscented transformation.…”

Section: Introductionmentioning

confidence: 98%

Robust estimation of power quality disturbances using unscented H∞ filter

Sahoo

Dash

2015

International Journal of Electrical Power & Energy Systems

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“…In [37], by using a technique called 'embedded transform', the authors proposed a novel solution for H ∞ filtering of discrete nonlinear systems. More recently, in [38], a complex H ∞ filter for non-stationary signals was developed. The approaches in [37] and [38] are also complex, and a comparison with the EKF was shown only in simulations (with noise).…”

Section: Introductionmentioning

confidence: 99%

“…The approaches in [37] and [38] are also complex, and a comparison with the EKF was shown only in simulations (with noise). In [38], the EKF showed comparable accuracy to the H ∞ observer and required slightly less computational time.…”

Section: Introductionmentioning

confidence: 99%

A hybrid EKF/KF state estimator for a skid-steered ATV

Javed

Owen

Biglarbegian

et al. 2013

Vehicle System Dynamics

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This article presents a novel state estimation structure developed for a skid-steered, six-wheeled, ARGO® all-terrain vehicle (ATV). The ARGO ATV is a teleoperated unmanned ground vehicle custom fitted with an inertial measurement unit, wheel encoders and a Geographic Positioning System (GPS). This paper offers the following contributions: (1) a hybrid extended Kalman filter/Kalman filter state estimation technique that estimates the motion, orientation and wheel slips for an ARGO and (2) a virtual GPS point generation algorithm that can be used to adjust position estimates using a GPS sensor. Our field experiments reveal that the proposed estimation structure is able to estimate the position, velocity, orientation and longitudinal slip of the ARGO with a reasonable amount of accuracy. In addition, the proposed estimation structure is well suited for online applications and can also incorporate offline virtual GPS data to further improve the accuracy of the position estimates. The proposed estimation structure is also capable of estimating the longitudinal slip for every wheel of the ARGO.

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Frequency estimation of distorted non-stationary signals using complex H∞ filter

Cited by 12 publications

References 25 publications

On LS-Based Power Frequency Estimation Algorithms

On LS-Based Power Frequency Estimation Algorithms

Robust estimation of power quality disturbances using unscented H∞ filter

A hybrid EKF/KF state estimator for a skid-steered ATV

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