A robust variable step-size LMS-like algorithm for a second-order adaptive IIR notch filter for frequency detection

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dc.contributor.authorPunchalard, R.
dc.contributor.authorBenjangkaprasert, C.
dc.contributor.authorAnantrasirichai, N.
dc.contributor.authorJanchitrapongvej, K.
dc.date.accessioned2026-08-06T09:50:56Z
dc.date.available2026-08-06T09:50:56Z
dc.date.issued2001-01-01
dc.description.abstractThe best adaptive algorithm requires fast convergence speed, low variance, unbias and low steady-state mean square error (MSE) in both low and high signal-to-noise ratio (SNR) situations. We have proposed a robust variable step-size LMS-like algorithm (VS-LMS-L) for a second-order adaptive IIR notch filter for frequency detection in radar, sonar and communication systems. This algorithm is compared with the conventional LMS-like algorithm called the plain gradient algorithm (PG). The time-varying step-size μ(n) is adjusted by using the square of the time-averaged estimate of autocorrelation of the present output signal y(n) and the past one y(n-1). This technique can reject the effect of the uncorrelated noise sequence on the step-size update, resulting in a small MSE due to the small final μ(n). Moreover, this algorithm can also improve the convergence speed by comparison with the PG at the same MSE value.
dc.identifier.citationIEEE Workshop on Signal Processing Advances in Wireless Communications Spawc, 2001-January, 232-234, 2001
dc.identifier.doi10.1109/SPAWC.2001.923890
dc.identifier.other2-s2.0-34547315877
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/382
dc.sourceIEEE Workshop on Signal Processing Advances in Wireless Communications Spawc
dc.subjectAdaptive algorithm
dc.subjectAdaptive filters
dc.subjectConvergence
dc.subjectFrequency
dc.subjectIIR filters
dc.subjectMean square error methods
dc.subjectRobustness
dc.subjectSignal to noise ratio
dc.subjectSonar detection
dc.subjectSteady-state
dc.titleA robust variable step-size LMS-like algorithm for a second-order adaptive IIR notch filter for frequency detection
dc.typeConference Paper

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