Direct frequency estimation based adaptive algorithm for a second-order adaptive FIR notch filter

This work deals with the problem of the frequency estimation of a sinusoidal signal corrupted by broad-band noise. The direct frequency estimation based adaptive algorithm for a second-order adaptive finite impulse response (FIR) notch filter (AFNF) is thus proposed. The proposed algorithm employs t...

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Veröffentlicht in:Signal processing Jg. 88; H. 2; S. 315 - 325
Hauptverfasser: Punchalard, R., Lorsawatsiri, A., Loetwassana, W., Koseeyaporn, J., Wardkein, P., Roeksabutr, A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.02.2008
Elsevier Science
Schlagworte:
Applied sciences
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
FIR notch filter
Gradient algorithm
Information, signal and communications theory
Signal and communications theory
Signal, noise
Telecommunications and information theory
Unbiased
Unbiased
Gradient algorithm
FIR notch filter
Wide band noise
Performance evaluation
Second order
Parameter estimation
Adaptive algorithm
Signal estimation
Frequency estimation
Adaptive filter
Difference equation
Steady state
Implementation
Mean square error
Notch filter
Digital filter
Simulation
Finite impulse response filter
Sinusoidal signal
State estimation
Gradient method
ISSN:0165-1684, 1872-7557
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Beschreibung
Zusammenfassung:This work deals with the problem of the frequency estimation of a sinusoidal signal corrupted by broad-band noise. The direct frequency estimation based adaptive algorithm for a second-order adaptive finite impulse response (FIR) notch filter (AFNF) is thus proposed. The proposed algorithm employs the bias removal technique to remove the bias existing in the estimated parameter. The performances including the rate of convergence and the mean square error (MSE) can be easily controlled by using only one parameter, i.e., step size parameter. Moreover, the proposed filter is simple to implement and suitable for real-time applications. In addition, the difference equations for the convergence in the mean and mean square, and the closed form expressions for the steady-state estimation bias and MSE are also carried out. Finally, the simulation results are provided to confirm the theoretical analysis.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2007.08.004