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Study for the Performance Improvement of Adaptive Filtering Algorithm based on Minimizing the Mean-Square Analysis

Title
Study for the Performance Improvement of Adaptive Filtering Algorithm based on Minimizing the Mean-Square Analysis
Authors
신재욱
Date Issued
2014
Publisher
포항공과대학교
Abstract
This work presents several algorithms to improve the performance of the adaptive filter based on minimizing the mean-square-deviation (MSD). Chapter 2 presents a normalized subband adaptive filter algorithm with a variable step size based on the MSD analysis. Since the spectrum of each input signal in subbands is close to that of white noise, the MSD can be approximated. The step size in this study is chosen such that the MSD undergoes the largest decrease from one iteration to the next, which leads to a fast convergence rate and a small misalignment. Simulation results confirm that the proposed algorithm outperforms the existing algorithms in the literature. Chapter 3 presents a MSD analysis of the periodic affine projection algorithm (P-APA) and two update-interval selection methods to achieve improved performance in terms of the convergence and the steady-state error. The MSD analysis of the P-APA considers the correlation between the weight error vector and the measurement noise vector. Using this analysis, it is verified that the update interval governs the trade-off between the convergence rate and the steady-state errors in the P-APA. To overcome this drawback, the proposed APAs increase the update interval when the adaptive filter reaches the steady state. Consequently, these algorithms can reduce the overall computational complexity. The simulation results show that the proposed APAs perform better than the previous algorithms. Chapter 4 presents a variable step-size affine projection sign algorithm (APSA) based on the minimization of MSD. Because the proposed algorithm calculates the optimum step size in every iteration, it ensures the improved performance in aspect of convergence rate and misalignment compared with the conventional APSAs. The proposed algorithm is tested in the system identification scenario including impulsive noise. Chapter 5 presents a variable step-size sign subband adaptive filter (SSAF) based on the minimization of MSD. In the process of minimizing the MSD, because it is not feasible to know the exact value of the MSD, the step size is derived by minimizing the upper bound of the MSD in each iteration. The proposed algorithm uses this step size in the SSAF update equation so as to improve the filter performance in terms of the convergence rate and the steady-state estimation error. Simulation results show that the proposed algorithm performs better than the previous algorithms.
본 연구에서는 무게 추측 차이 평균 제곱 (MSD)를 줄이는 방법을 이용하여 적응형 필터의 성능을 개선하는 알고리즘을 제안한다. 먼저 Chapter 2에서는 MSD 분석을 통한 normalized subband adaptive filter용 가변 스텝사이즈 알고리즘을 제안한다. 각 subband를 통과한 입력신호는 white noise와 스펙트럼이 유사하기 때문에 이를 이용하여 MSD를 근사적으로 알아낸다. 그리고 근사적으로 구한 MSD를 가장 빠르게 줄이는 스텝사이즈를 선택하여 사용함으로써 빠른 수렴성능과 작은 정상 상태 오차를 가지는 알고리즘을 제안한다. Chapter 3에서는 periodic affine projection algorithm (P-APA) 의 MSD 분석방법을 제안하고 이를 이용하여 두 가지의 갱신 주기 선택 방법을 제안한다. P-APA의 MSD를 분석할 때 무게 오차 벡터와 계측 노이즈 벡터 사이의 연관성을 고려하였으며 이를 이용하여 P-APA의 갱신 주기에 따른 성능변화를 입증하였다. 또한 P-APA의 성능을 높이기 위한 적절한 갱신 주기를 찾는 방법을 제안하였다. Chapter 4에서는 affine projection sign algorithm APSA)의 MSD를 최소화 시키는 가변 스텝사이즈 알고리즘을 제안한다. APSA는 스텝사이즈에 따라 성능변화가 일어나기 때문에 빠른 수렵성능과 작은 정상 상태 오차를 가지는 알고리 즘을 제안하다. Chapter 5에서는 sign subband adaptive filter의 MSD를 최소화 시키는 가변 스텝사이즈 알고리즘을 제안한다. SSAF의 정확한 MSD를 구하기 어렵기 때문에 MSD의 상한을 최소화 하는 스텝사이즈를 구하여 사용한다.
URI
http://postech.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000001738286
http://oasis.postech.ac.kr/handle/2014.oak/2277
Article Type
Thesis
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