Robust Processing of Nonstationary Signals
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Editorial Robust Processing of Nonstationary Signals Igor Djurovi´c,1 Ljubiˇsa Stankovi´c,1 Markus Rupp (EURASIP Member),2 and Ling Shao3 1 Electrical
Engineering Department, University of Montenegro, Cetinjski br.2, 81000 Podgorica, Montenegro of Communications and Radio Engineering, Vienna University of Technology, Gusshausstrape 25/389, 1040 Wien, Austria 3 Philips Research Laboratories, 5656 AE Eindhoven, The Netherlands 2 Institute
Correspondence should be addressed to Igor Djurovi´c, [email protected] Received 17 August 2010; Accepted 17 August 2010 Copyright © 2010 Igor Djurovi´c et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Techniques for processing signals corrupted by nonGaussian noise are referred to as the robust techniques. They have been established and used in science in the past 40 years. The principles of robust statistics have found fruitful applications in numerous signal-processing disciplines especially in digital image processing and signal processing for communications. Median, myriad, meridian, L filters (with their modifications), and signal-adaptive realizations form a powerful toolbox for diverse applications. All of these filters have lowpass characteristic. This characteristic limits their application in analysis of diverse nonstationary signals where impulse, heavy-tailed, or other forms of the non-Gaussian noise can appear: FM, radar and speech signal processing, and so forth. Recent research activities and studies have shown that combination of nonstationary signals and non-Gaussian noise can be observed in some novel emerging applications such as internet traffic monitoring and digital video coding. Several techniques have been recently proposed for handling signal filtering, parametric/nonparametric estimation, and feature extraction, of nonstationary and signals with high-frequency content corrupted by non-Gaussian noise. One approach is based on filtering in time domain. Here, the standard median/myriad forms are modified in such a manner to allow negative and complex-valued weights. This group of techniques is able to produce all filtering characteristics: high-pass, stop-band, and band-pass. As an alternative, the robust filtering techniques are proposed in spectral (frequency-Fourier, DCT, wavelet, or in the timefrequency) domain. The idea is to determine robust transforms having ability to eliminate or surpass influence of nonGaussian noise. Then, filtering, parameter estimation, and/or feature extraction is performed using the standard means. Other alternatives are based on the standard approaches
(optimization, iterative, and ML strategies) modified for nonstationary signals or signals with high-frequency content. Since these techniques are increasingly popular, the goal of this special issue is to review and compare them, propose new techniques, study novel application fields, and to consider their impl
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