Prof. Zoubir und Dr.-Ing. Muma erhalten IEEE Signal Processing Magazine Best Paper Award
16.01.2018
Der Beitrag „Robust Estimation in Signal Processing: A tutorial-style treatment of fundamental concepts“ von Abdelhak M. Zoubir , Visa Koivunen, Yacine Chakhchoukh und Michael Muma wird mit dem IEEE Signal Processing Magazine Best Paper Award ausgezeichnet. Der Artikel wurde im IEEE Signal Processing Magazine, Volume 29, No. 4, Juli 2012, veröffentlicht.
Die Signal Processing Society wurde 1948 als erste IEEE Society gegründet und ist heute mit weltweit mehr als 19.000 Mitgliedern die viertgrößte der 39 IEEE Societies. Die Preisträger der 2017 Signal Processing Society Arwards werden die Auszeichnungen während der ICASSP 2018 in Calgary, Alberta, Canada, erhalten.
Herzlichen Glückwunsch an alle Authoren!
Kurzzusammenfassung:
„The word robust has been used in many contexts in signal processing. Our treatment concerns statistical robustness, which deals with deviations from the distributional assumptions. Many problems encountered in engineering practice rely on the Gaussian distribution of the data, which in many situations is well justified. This enables a simple derivation of optimal estimators. Nominal optimality, however, is useless if the estimator was derived under distributional assumptions on the noise and the signal that do not hold in practice. Even slight deviations from the assumed distribution may cause the estimator's performance to drastically degrade or to completely break down. The signal processing practitioner should, therefore, ask whether the performance of the derived estimator is acceptable in situations where the distributional assumptions do not hold. Isn't it robustness that is of a major concern for engineering practice? Many areas of engineering today show that the distribution of the measurements is far from Gaussian as it contains outliers, which cause the distribution to be heavy tailed. Under such scenarios, we address single and multichannel estimation problems as well as linear univariate regression for independently and identically distributed (i.i.d.) data. A rather extensive treatment of the important and challenging case of dependent data for the signal processing practitioner is also included. For these problems, a comparative analysis of the most important robust methods is carried out by evaluating their performance theoretically, using simulations as well as real-world data.“
