An adaptive notch filter with improved tracking properties

Dragosevic, M.V.; Stanković, S.

doi:10.1109/78.414768

Cited by 69 publications

(20 citation statements)

References 9 publications

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“…These forgetting factors are known to play a crucial role in the estimation of the Hessian matrix and it has been pointed out that 2A(t) should be somewhat equal to the pole contraction factor p(t) [11,12]. Fixing A2L(t) at a constant value [8] doesn't appear to deteriorate the frequency estimation performance, while at the same time this approach reduces the variation of p(t) and generally provides a better time-domain estimation error.…”

Section: Frequency Transform Based Adaptive Notch Filtermentioning

confidence: 99%

Robust Notch Filtering by Combining Adaptation in both Time and Frequency

DeBrunner

2007

2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers

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A fully adaptive infinite impulse response (IIR) notch filter has been proposed before with a very good tracking property over a certain frequency range [7,8]. It is shown that when the frequency to be notched is closed to DC or the Nyquist frequency (fr,) the performance of the IIR notch filter is degraded. A finite impulse response (FIR) notch filter with equal number ofcoefficients exhibits a reversed performance: the tracking performance is worse than the IIR notch filter in those frequencies far from DC and fn but better than the IIR notch filter in those frequencies close to either DC orfn.Hence, a combination of these two filters is proposed in this paper to improve the tracking performance of the notch filter over the total range of frequencies. Computer simulations show the improved performance of the combined adaptive filter. The design is also simple in computation and robust in tracking a time-varying sinusoidal signal.

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Section: Frequency Transform Based Adaptive Notch Filtermentioning

confidence: 99%

Robust Notch Filtering by Combining Adaptation in both Time and Frequency

DeBrunner

2007

2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers

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“…A block diagram implementation of the equation set (6) is shown in Figure 2. The top branch is the amplitude estimator while the bottom loop estimates the phase angle.…”

Section: Structure and Mathematical Propertiesmentioning

confidence: 99%

“…(9), the oscillators autonomously work at the fixed frequency of o o while in the system of (6), the oscillators are controlled by ' f fðtÞ which in turn is determined/controlled by the system through a closed-loop. In other words, imposing the 'm 1 ¼ m 2 A ¼ Constant' condition is equivalent to breaking the loop and hence turning the closed-loop system of (6) into an open-loop system exhibiting no adaptive nature. (6) is framed in a two-dimensional polar co-ordinate system of A and f: The phase plane diagram of this dynamical system is considered in this co-ordinate system in which a pure sinusoidal function…”

Section: Proofmentioning

confidence: 99%

“…is a solution for the differential equations (6). This means that the periodic orbit coincides with the desired component.…”

Section: Proofmentioning

confidence: 99%

“…Adaptive notch filtering (ANF) finds applications in problems in which it is desirable to extract or eliminate individual sine waves from an observed set of data or to estimate their unknown frequencies [1][2][3][4][5][6][7][8][9][10][11]. The ANF falls in the category of on-line processing techniques in contrast to off-line methods such as spectral estimation techniques, which are most commonly based on the discrete Fourier transform (DFT) [12].…”

Section: Introductionmentioning

confidence: 99%

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Performance characterization of a non‐linear system as both an adaptive notch filter and a phase‐locked loop

Karimi-Ghartemani

Ziarani

2004

Adaptive Control & Signal

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The behaviour of a non-linear dynamical system is described. The system may be characterized as an adaptive notch filter, or alternatively, as a phase-locked loop. Either way, the system has the inherent capability of directly providing estimates of the parameters of the extracted sinusoidal component of its input signal, namely its amplitude, phase and frequency. The structure and mathematical properties of the system are presented for two cases of fixed-frequency and varying-frequency operation. The effects of parameter setting of the system on its performance are studied in detail using computer simulations. Transient and steady-state behaviour of the system are studied in the presence of noise. Simplicity of structure, high noise immunity and robustness and the capability of direct estimation of amplitude, phase and frequency are the salient features of the system when envisaged as an adaptive notch filter or a phaselocked loop.

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An Adaptive Notch Gain Using an Inverse Notch Filter and a Linear Prediction Filter

Nakamura

Kawamura

Iiguni

2017

Elect Comm in Japan

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This paper proposes an adaptive notch filter that automatically adjusts the notch frequency and notch gain, where they are the null frequency and the depth of the null, respectively. The proposed notch filter consists of an inverse notch filter and a linear prediction filter, and gives an appropriate filter gain which can eliminate a sinusoidal noise and extract only a wide-band signal even if they share the same frequency. We compare the capability of the proposed notch filter with traditional methods. Simulation results show that the proposed system improves the SNR in comparison to the conventional methods. C⃝ 2017 Wiley Periodicals, Inc. Electron Comm Jpn, 100(2): 58-67, 2017; Published online in Wiley Online Library (wileyonlinelibrary.com).

show abstract

An adaptive notch filter with improved tracking properties

Cited by 69 publications

References 9 publications

Robust Notch Filtering by Combining Adaptation in both Time and Frequency

Robust Notch Filtering by Combining Adaptation in both Time and Frequency

Performance characterization of a non‐linear system as both an adaptive notch filter and a phase‐locked loop

An Adaptive Notch Gain Using an Inverse Notch Filter and a Linear Prediction Filter

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