A Novel Adaptive Nonlinear Filter-Based Pipelined Feedforward Second-Order Volterra Architecture
ABSTRACT Due to the computational complexity of the Volterra filter, there are limitations on the implementation in practice. In this paper, a novel adaptive joint process filter using pipelined feedforward second-order Volterra architecture (JPPSOV) to reduce the computational burdens of the Volterra filter is proposed. The proposed architecture consists of two subsections: nonlinear subsection performing a nonlinear mapping from the input space to an intermediate space by the feedforward second-order Volterra (SOV), and a linear combiner performing a linear mapping from the intermediate space to the output space. The corresponding adaptive algorithms are deduced for the nonlinear and linear combiner subsections, respectively. Moreover, the analysis of theory shows that these adaptive algorithms based on the pipelined architecture are stable and convergence under a certain condition. To evaluate the performance of the JPPSOV, a series of simulation experiments are presented including nonlinear system identification and predicting of speech signals. Compared with the conventional SOV filter, adaptive JPPSOV filter exhibits a litter better convergence performance with less computational burden in terms of convergence speed and steady-state error.
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ABSTRACT: Rapid advances in the field of signal processing are revolutionizing algorithms. This paper describes the concept of adaptive noise cancellation, an alternative method of estimating signals corrupted by additive noise or interference. The Adaptive algorithms are used to improve the convergence rate, signal to noise ratio, stability, mean square error, steady state behavior, tracking, misadjustment has become a focus on digital signal processing. Accurate cancellation of noise in signal processing is a key step of adaptive filter algorithms. In this paper, Acoustic echo cancellation problem was discussed out of different noise cancellation techniques by concerning different parameters with their comparative results. The results shown are using some specific algorithms. The results show, improving convergence rate with less no of taps is the most difficult phase in signal processing applications for the perfect working of any system.Computational Intelligence & Computing Research (ICCIC), 2012 IEEE International Conference on; 01/2012
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ABSTRACT: The electroencephalogram (EEG) is the most widely used method for diagnosis of brain diseases, where a good quality of recordings allows the proper interpretation and identification of physiological and pathological phenomena. However, EEG recordings are often contaminated by different kinds of noise. These annoying signals limit severely brain recording utility and, hence, have to be removed. To deal with this problem, in this paper an adaptive filtering framework is proposed for the enhancing of brain signal recordings. This new method is capable of reducing muscle and baseline noise in EEG signals with low EEG distortion and high noise cancellation. The advantages of the proposed method are demonstrated on real and synthetic brain signals with comparisons made to several benchmark methods. Results show that the proposed approach is preferable to the other systems by achieving a better trade-off between deleting noises and preserving inherent brain activities.Computers & Electrical Engineering 07/2013; 39(5):1561-1570. DOI:10.1016/j.compeleceng.2012.11.006 · 0.99 Impact Factor
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ABSTRACT: In this paper, the problem of parameter estimation of the combined radar signal adopting chaotic pulse position modulation (CPPM) and linear frequency modulation (LFM), which can be widely used in electronic countermeasures, is addressed. An approach is proposed to estimate the initial frequency and chirp rate of the combined signal by exploiting the second-order cyclostationarity of the intra-pulse signal. In addition, under the condition of the equal pulse width, the pulse repetition interval (PRI) of the combined signal is predicted using the low-order Volterra adaptive filter. Simulations demonstrate that the proposed cyclic autocorrelation Hough transform (CHT) algorithm is theoretically tolerant to additive white Gaussian noise. When the value of signal noise to ratio (SNR) is less than -4 dB, it can still estimate the intra-pulse parameters well. When SNR = -3 dB, a good prediction of the PRI sequence can be achieved by the Volterra adaptive filter algorithm, even only 100 training samples. (C) 2013 Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA.Chinese Journal of Aeronautics 08/2013; 26(4):986-992. DOI:10.1016/j.cja.2013.06.008 · 0.69 Impact Factor