Robust Processing of Nonstationary Signals

Call for Papers

Techniques for processing signals corrupted by non-Gaussian noise are referred to as the robust techniques. They are 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 the signal filtering, parametric/nonparametric estimation, feature extraction of nonstationary and signals with high-frequency content corrupted by non-Gaussian noise. One approach is based on filtering in the 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: highpass, stopband, and bandpass. As an alternative, the robust filtering techniques are proposed in spectral (frequency- Fourier, DCT, wavelet, or in the time-frequency) domain. The idea is to determine robust transforms having the ability to eliminate or surpass influence of non-Gaussian 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, 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 consider their implementations.

Topics of interest include, but are not limited to:

• Robust statistical signal processing (estimation, detection, decisions)
• Robust tracking, classification and control
• Performance analysis, comparison, benchmark setting, and achievable bounds
• Robust parametric/non-parametric estimation, filtering, and feature extraction of nonstationary signals
• Robust learning and adaptive robust techniques
• Fast software and hardware realizations
• Applications

Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/asp/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/ according to the following timetable:

Lead Guest Editor

• Igor Djurović, Department of Electrical Engineering, University of Montenegro, Cetinjski put bb, 81000 Podgorica, Montenegro

Guest Editors

• Ljubiša Stanković, Department of Electrical Engineering, University of Montenegro, Cetinjski put bb, 81000 Podgorica, Montenegro
• Markus Rupp, Institute of Communications and Radio Engineering, Vienna University of Technology, Gusshausstrasse 25/389, 1040 Wien, Austria
• Ling Shao, Philips Research Labaratories, 5656 AE Eindhoven, The Netherlands