Frequency-response-shaped LMS adaptive filter

Kükrer, Osman; Hocanin, Aykut

doi:10.1016/j.dsp.2006.07.004

Cited by 15 publications

(7 citation statements)

References 11 publications

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“…where λ max is the maximum eigenvalue of R. R = F + R xx , where R xx is the autocorrelation matrix of the input signal. It has also been shown in [8] that the error variance of the FRS-LMS algorithm can be made less than that of the standard LMS, provided that µ < 1 λmax . Using the fast computation method in [8], the overall complexity of the proposed algorithm is comparable to that of the Leaky-LMS algorithm.…”

Section: The Frs-lms Algorithmmentioning

confidence: 99%

“…The signal s(k) is assumed to be corrupted by AWGN. Simulations of the FRS-LMS algorithm were done using the fast computation method in [8] with µ = 0.00025, filter length N = 32 taps. The weight function used in the simulations is shown in Fig.…”

Section: Additive White Gaussian Noise (Awgn)mentioning

confidence: 99%

“…Leaky-LMS algorithm [7], is simple and has good performance in impulsive noise environments even at relatively small signal-to-noise ratios (SNR's). The FRS-LMS algorithm has been proposed as a generalized version of the Leaky LMS in [8].…”

Section: Introductionmentioning

confidence: 99%

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Performance of the Frequency-Response-Shaped LMS Algorithm in Impulsive Noise

Ahmad

Hocanin

Kükrer

2007

2007 IEEE International Conference on Signal Processing and Communications

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The performance of the Frequency-Response-Shaped Least Mean Square (FRS-LMS) adaptive algorithm in estimating a sinusoidal signal in impulsive and correlated noise is investigated. The algorithm does not require a priori knowledge about the nominal Gaussian process and is able to adapt to changes in the environment. The performance of the FRS-LMS is compared to that of the Leaky-LMS algorithms in terms of Mean Square Error (MSE) and convergence speed. The results indicate that while the FRS-LMS and the Leaky LMS algorithms perform similarly in AWGN, the FRS-LMS provides superior performance in impulsive and correlated noise environments. The performance gain is due to the frequency shaping and outlier reduction properties of the algorithm.

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Section: The Frs-lms Algorithmmentioning

confidence: 99%

Section: Additive White Gaussian Noise (Awgn)mentioning

confidence: 99%

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Performance of the Frequency-Response-Shaped LMS Algorithm in Impulsive Noise

Ahmad

Hocanin

Kükrer

2007

2007 IEEE International Conference on Signal Processing and Communications

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“…This improves convergence speed but with increased computations. Some of the most popular of those methods may include: Modified NLMS [14], [15], Leaky LMS [16], Sign Error and Sign Data LMS [17], [18], Variable step size LMS [19], frequency response shaped LMS [20], Hybrid LMS [21], Absolute Average Error Adjusted Step-Size LMS [2], [22], Filter Proportionate Arctangent framework-based LMS (FP-ALMS) [23], proportionate NLMS (PNLMS) [24] and other algorithms. The use of evolutionary techniques is also suggested to improve convergence of adaptive filters in the denoising operation of medical signals [25].…”

Section: Introductionmentioning

confidence: 99%

Improved LMS Performance for System Identification Application

Mamdoh Abdulatef,

Rami Alomari,

Ahmed Mahmod

2024

IJCDS

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The adaptive filtering algorithm of Normalized Least Mean Square (NLMS) is known to be highly efficient in terms of requiring less number of iterations compared to the reference Least Mean Square (LMS) method, at the cost of having increased computations. This is because, while a fixed value is assigned for the step size in LMS method, the step size in NLMS is continuously updated to a new value with each iteration. The performance of the gradient descent method of LMS is found to be highly dependent on the step size value assigned at the beginning of the iterations. Throughout the literature, the whole range of LMS step size within which stability is assured is usually suggested and studied, while the selection of the most suitable value of the step size within this range is still not thoroughly studied. In this work, a new method to help specifying the exact value of step size in LMS is proposed. This method is resembled by using the step size value assigned in the first iteration in NLMS method to set the value of the step size in LMS. This is found through the results to be highly effective in approaching NLMS behavior without having to increase computational burden. The performance of the proposed method is evaluated using the Mean Square Error (MSE), Weight Difference (WD) and absolute error after 1000 iterations. Relying on this way to specify the LMS step size can provide simplicity, accuracy and high convergence speed, not only for system identification, but also for many other adaptive filtering applications.

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“…The main drawback of RLS is its poor performance when the filter to be estimated changes its statistical properties. A LMS variant named frequency-response-shaped least mean squares (FRS-LMS) was proposed in Kukrera & Hocanin (2006) and shown to have good convergence properties. The FRS-LMS algorithm has improved performance when a sinusoidal input signal is corrupted by correlated noise.…”

Section: Variants Of Lmsmentioning

confidence: 99%