A simplified FTF-type algorithm for adaptive filtering

Benallal, Ahmed; Benkrid, Abdelhak

doi:10.1016/j.sigpro.2006.08.013

Cited by 21 publications

(15 citation statements)

References 12 publications

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“…The adaptation gain for the traditional NLMS algorithm, with the stability condition 0 < < 2, is approximately given by [2] c NLMS .n/ N 2…”

Section: Stability Conditionmentioning

confidence: 99%

“…In practice, it is therefore valuable to reduce the computational cost for real-time implementation. In previous works [2,3], we have already reduced the complexity of the fast transversal filter algorithm (FTF) by using only a reduced-size forward predictor to compute the adaptation gain with 2N+5P multiplications. In this paper, in order to find additional computational reductions, we use the same FTF algorithm where the adaptation gain is obtained by discarding completely both forward and backward predictors.…”

Section: Introductionmentioning

confidence: 99%

“…where Q c N .n/and .n/, called the dual Kalman gain and the likelihood variable, respectively, are updated by propagating two vectors a N .n/ and b N .n/ called, respectively, forward and backward predictors [1,2]:…”

Section: Introductionmentioning

confidence: 99%

“…Several numerical solutions of stabilization, with stationary signals, are proposed in the literature. The computational cost of the stabilized FTF (SFTF) algorithm is in the order of 8N operations per sample [2].…”

Section: Introductionmentioning

confidence: 99%

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A fast convergence normalized least‐mean‐square type algorithm for adaptive filtering

Benallal

Arezki

2013

Adaptive Control & Signal

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A new adaptive algorithm with fast convergence and low complexity is presented. By using the calculation structure of the dual Kalman variables of the fast transversal filter algorithm and a simple decorrelating technique for the input signal, we obtain an algorithm that exhibits faster convergence speed and enhanced tracking ability compared with the normalized least-mean-square algorithm with similar computational complexity.A. BENALLAL AND M. AREZKI CONCLUSIONIn this paper, a new FNLMS-type algorithm for adaptive filters is presented. The adaptation gain for the new algorithm is obtained by simplification of the FTF adaptation gain calculation. The amount of computation, 2N multiplications per sample, is the same for the proposed algorithm as for the NMLS algorithm. In all presented simulations, the proposed algorithm performs better than NLMS. It exhibits faster convergence and enhanced tracking ability compared with the NLMS algorithm.

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“…The adaptation gain for the traditional NLMS algorithm, with the stability condition 0 < < 2, is approximately given by [2] c NLMS .n/ N 2…”

Section: Stability Conditionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A fast convergence normalized least‐mean‐square type algorithm for adaptive filtering

Benallal

Arezki

2013

Adaptive Control & Signal

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“…Therefore, in order to remove the noises completely and improve the detection accuracy and sensitivity, we propose a novel self-adaptive IR gas detection system in this paper. The self-adaptive modern digital filter which is based on the least square fast transverse filtering (LS-FTF) algorithm [9] instead of the classic filter is introduced, and an additional noise channel besides the detection channel and reference channel is used.…”

mentioning

confidence: 99%

Self-adaptive CH4 concentration detection system based on infrared spectrum absorption principle

Xin

Zheng

et al. 2011

Optoelectron. Lett.

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Considering that the noises resulting from low modulation frequency are serious and cannot be totally eliminated by the classic filters, a novel infrared (IR) gas concentration detection system based on the least square fast transverse filtering (LS-FTF) self-adaptive modern filter structure is proposed. The principle, procedure and simulation on the LS-FTF algorithm are described. The system schematic diagram and key techniques are discussed. The procedures for the ARM7 processor, including LS-FTF and main program, are demonstrated. Comparisons between the experimental results of the detection system using the LS-FTF algorithm and those of the system without using this algorithm are performed. By using the LS-FTF algorithm, the maximum detection error is decreased from 14.3% to 5.4%, and also the detection stability increases as the variation range of the relative error becomes much smaller. The proposed LS-FTF self-adaptive denoising method can be of practical value for mid-IR gas detection, especially for weak signal detection.

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New efficient adaptive fast transversal filtering (FTF)‐type algorithms for mono and stereophonic acoustic echo cancelation

Djendi

2014

Adaptive Control & Signal

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This paper addresses the field of stereophonic acoustic echo cancelation (SAEC) by adaptive filtering algorithms. Recently, simplified versions of the fast transversal filter (SFTF)-type algorithm has been proposed. In this paper, we propose two major contributions. In the first contribution, we propose two new FTF-type algorithms with low complexity and good convergence speed characteristics. These two proposed algorithms are mainly on the basis of a forward prediction scheme to estimate the so called dual Kalman gain, which is inherent in the filtering part update. This computation complexity is achieved by the introduction of new relations for the computation of the likelihood variables that are simple and lead to further simplifications on the prediction part of the two proposed algorithms. In the second contribution, we propose to adapt then apply these four new SFTF-type algorithms, (the two proposed algorithms in this paper and their original versions) in the SAEC applications. A fair comparison of the proposed algorithms with the original SFTF and the normalized least mean square algorithms, in mono and SAEC applications, is presented. Copyright 274 M. DJENDI Recently, a different approach for the SAEC problems (inter-channel coherence, adaptive filter lengths, etc.) was proposed in [18,19] by using the widely linear model, which is assigned to the processing of noncircular complex valued signals. A new hybrid solution to the same problem that uses frequency shifts to improve the performance of the state-of-art solution based on addition of half-wave rectified signals is presented in [20]. The authors in [21] proposed a windowing frequency domain adaptive filter, and an up-sampling block transforms preprocessing, to solve these constraints. The proposed adaptive filter uses windowing functions with smooth cutoff property to reduce the spectral leakage during filter updating, so the utilization of the independent noise introduced by preprocessing in SAEC can be increased. The proposed preprocessing is operated in short blocks with low processing delay, and it uses frequency-domain up-sampling to meet the minimal block length requirement given by the band limit of simultaneous masking. Another very recent and novel approach to SAEC is proposed in [22] and based on a combination of psychoacoustic effects. The principle of the proposed structure is to use an approach based on the 'missing-fundamental' phenomenon at low frequencies (below 500 Hz) and a modified phase modulation approach at higher frequencies.A basic scheme for SAEC is given in Figure 1, where we illustrate the concept with a transmission room on the left and a receiving room on the right [16,17,23,24]. In Figure 1, when we generate a signal in the transmission room, the two microphones mic 1 and mic 2 receive two sorts of signals: (i) the direct source signal modified by the path g 1 (then received by mic 1 / and g 2 (then received by mic 2 /, respectively, and (ii) the existing diffuse noises n 1 and n 2 in the transmission room and received ...

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A simplified FTF-type algorithm for adaptive filtering

Abstract: ABSTRACT

Cited by 21 publications

References 12 publications

A fast convergence normalized least‐mean‐square type algorithm for adaptive filtering

A fast convergence normalized least‐mean‐square type algorithm for adaptive filtering

Self-adaptive CH4 concentration detection system based on infrared spectrum absorption principle

New efficient adaptive fast transversal filtering (FTF)‐type algorithms for mono and stereophonic acoustic echo cancelation

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