843 research outputs found
Robust Andrew's sine estimate adaptive filtering
The Andrew's sine function is a robust estimator, which has been used in
outlier rejection and robust statistics. However, the performance of such
estimator does not receive attention in the field of adaptive filtering
techniques. Two Andrew's sine estimator (ASE)-based robust adaptive filtering
algorithms are proposed in this brief. Specifically, to achieve improved
performance and reduced computational complexity, the iterative Wiener filter
(IWF) is an attractive choice. A novel IWF based on ASE (IWF-ASE) is proposed
for impulsive noises. To further reduce the computational complexity, the
leading dichotomous coordinate descent (DCD) algorithm is combined with the
ASE, developing DCD-ASE algorithm. Simulations on system identification
demonstrate that the proposed algorithms can achieve smaller misalignment as
compared to the conventional IWF, recursive maximum correntropy criterion
(RMCC), and DCD-RMCC algorithms in impulsive noise. Furthermore, the proposed
algorithms exhibit improved performance in partial discharge (PD) denoising.Comment: 5 pages, 5 figure
GENETIC FUZZY FILTER BASED ON MAD AND ROAD TO REMOVE MIXED IMPULSE NOISE
In this thesis, a genetic fuzzy image filtering based on rank-ordered absolute
differences (ROAD) and median of the absolute deviations from the median (MAD) is
proposed. The proposed method consists of three components, including fuzzy noise
detection system, fuzzy switching scheme filtering, and fuzzy parameters
optimization using genetic algorithms (GA) to perform efficient and effective noise
removal. Our idea is to utilize MAD and ROAD as measures of noise probability of a
pixel. Fuzzy inference system is used to justify the degree of which a pixel can be
categorized as noisy. Based on the fuzzy inference result, the fuzzy switching scheme
that adopts median filter as the main estimator is applied to the filtering. The GA
training aims to find the best parameters for the fuzzy sets in the fuzzy noise
detection.
From the experimental results, the proposed method has successfully removed
mixed impulse noise in low to medium probabilities, while keeping the uncorrupted
pixels less affected by the median filtering. It also surpasses the other methods, either
classical or soft computing-based approaches to impulse noise removal, in MAE and
PSNR evaluations. It can also remove salt-and-pepper and uniform impulse noise
well
New sequential partial-update least mean M-estimate algorithms for robust adaptive system identification in impulsive noise
The sequential partial-update least mean square (S-LMS)-based algorithms are efficient methods for reducing the arithmetic complexity in adaptive system identification and other industrial informatics applications. They are also attractive in acoustic applications where long impulse responses are encountered. A limitation of these algorithms is their degraded performances in an impulsive noise environment. This paper proposes new robust counterparts for the S-LMS family based on M-estimation. The proposed sequential least mean M-estimate (S-LMM) family of algorithms employ nonlinearity to improve their robustness to impulsive noise. Another contribution of this paper is the presentation of a convergence performance analysis for the S-LMS/S-LMM family for Gaussian inputs and additive Gaussian or contaminated Gaussian noises. The analysis is important for engineers to understand the behaviors of these algorithms and to select appropriate parameters for practical realizations. The theoretical analyses reveal the advantages of input normalization and the M-estimation in combating impulsive noise. Computer simulations on system identification and joint active noise and acoustic echo cancellations in automobiles with double-talk are conducted to verify the theoretical results and the effectiveness of the proposed algorithms. © 2010 IEEE.published_or_final_versio
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