[C3] Widely Linear Adaptive Processing of Noncircular Complex Signals
This research will provide a novel theoretical framework and enhanced practical solutions for adaptive processing (supervised and blind) of noncircular complex signals. Standard, widely used, solutions inherently assume second order circularity of signal distributions, and are therefore inadequate when the signals are observed through nonlinear sensors or as mixtures of sources, when the noise model is not doubly white, and when high resolution and enhanced separability are paramount. This will be achieved based on recent fundamental developments in the statistics of complex variables, called augmented complex statistics. The fundamental novelty in this work is the design of statistical signal processing techniques for the detection, estimation, and quanti cation of the degree of noncircularity of real world signals. This information will be used in conjunction with widely linear adaptive signal processing techniques, to enable optimal processing of complex signals with both circular and noncircular distributions.
The outcomes of this research will have an immediate impact in a variety of applications of signal detection, estimation, and adaptive signal processing, including those in interference supression, direction of arrival estimation, and blind extraction of sources of interest in real world scenarios.
Project Supervisor
Dr. Mandic received the Ph.D. degree in nonlinear adaptive signal processing in 1999 from Imperial College, London, London, U.K. He is now a Reader with the Department of Electrical and Electronic Engineering, Imperial College London, London, U.K. He has previously taught at the Universities of East Anglia, Norwich, Norfolk, U.K., and Banja Luka, Bosnia Herzegovina. He has written over 150 publications on a variety of aspects of signal processing and a research monograph on recurrent neural networks. He has been a Guest Professor at the Catholic University Leuven, Leuven, Belgium and Tokyo University of Agriculture and Technology (TUAT), and Frontier Researcher at the Brain Science Institute RIKEN, Tokyo, Japan. Dr. Mandic has been a Member of the IEEE Signal Processing Society Technical Committee on Machine Learning for Signal Processing, Associate Editor for IEEE Transactions on Circuits and Systems II, and Associate Editor for International Journal of Mathematical Modeling and Algorithms. He has won awards for his papers and for the products coming from his collaboration with industry.
