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Recursive inverse adaptive filtering techniques and applications

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  1. Tez No: 400231
  2. Yazar: MOHAMMAD MUSTAFA SHUKRİ AHMAD
  3. Danışmanlar: DOÇ. DR. AYKUT HOCANIN, PROF. DR. OSMAN KÜKRER
  4. Tez Türü: Doktora
  5. Konular: Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
  6. Anahtar Kelimeler: Adaptive Filters, LMS Algorithm, RLS Algorithm, RI Algorithm, CorrelatedNoise, Impulsive Noise
  7. Yıl: 2011
  8. Dil: İngilizce
  9. Üniversite: Doğu Akdeniz Üniversitesi-Eastern Mediterranean University
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Elektrik ve Elektronik Mühendisliği Ana Bilim Dalı
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 138

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Özet (Çeviri)

Adaptive filtering techniques are widely used to cope with the variations of systemparameters. In finite impulse response (FIR) adaptive filtering, the filter weights areupdated iteratively by minimizing the mean-square-error (MSE) of the differencebetween the desired response of the adaptive filter and its output. However, mostof the existing adaptive filters experience many difficulties; fixed-step size whichprovides poor performance in highly correlated environments, high computationalcomplexity, stability due to the inversion of the autocorrelation matrix, trackingability in non-stationary and impulsive noise environments.The novelty of this work resides in the derivation of a new FIR recursive inverse(RI) adaptive filtering algorithm. This algorithm has been proposed to overcomesome of the difficulties experienced with the existing adaptive filtering techniques.The approach uses a variable step-size and the instantaneous value of the autocorrelationmatrix in the coefficient update equation that leads to an improved performance.Avoiding the use of the inverse autocorrelation matrix, as the case of therecursive-least-squares (RLS) algorithm, would provide more stable performance.Convergence analysis of the algorithm has been presented. The ensemble-averagelearning curve of the RI algorithm is derived and compared with those of the RLSand least-mean-square (LMS) algorithms. A general fast implementation technique,which significantly reduces the computational complexity, of the RI algorithm ispresented. A robust version of the RI algorithm, which leads to an improved performance,in impulsive noise environments is presented. The effect of the forgettingfactor on the performance of the RI algorithm is investigated. Also, a twoiiidimensional (2D) version of the RI algorithm is introduced. Finally, a second-orderversion of the RI algorithm, which provides further improvement in the performance,is derived.The performance of the RI, fast RI, proposed robust RI (PRI), second order RIand 2D RI algorithms is compared to those of the standard LMS, normalized LMS(NLMS) , variable step size LMS (VSSLMS), discrete cosine transform LMS (DCTLMS),transform domain LMS with variable step-size (TDVSS), RLS, stabilizedfast transversal RLS (SFTRLS), robust RLS (RRLS) and proposed robust RLS algorithmsin additive white Gaussian noise (AWGN), correlated Gaussian noise andwhite and correlated impulsive noise, in noise cancellation, system identification,channel equalization, echo cancellation and image deconvolution setting, in stationaryand non-stationary environments. Simulations show that the RI algorithmand its variants outperform all the aforementioned algorithms as will be shown indetail later.

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