Spatio-Temporal Statistical Signal Processing For Blind Equalization and Source Separation

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This research is concerned with analysis and processing of stochastic signals received at multiple sensors from multiple sources with focus on blind equalization of digital communications signals and on blind separation of convolutive mixtures of independent sources (signals). Multiple-input multiple-output (MIMO) models of digital communication systems arise in a wide variety of communications applications: high-speed digital subscriber lines, multi-track digital magnetic recording, multiuser/multi-access communications systems, digital radio with diversity, dually polarized radio channels, multisensor sonar/radar systems, etc. MIMO channel modeling allows for a unified and optimal approach to design of MIMO equalizers/filters/combiners for suppression of intersymbol interference (ISI), cochannel and adjacent channel interferences (CCI and ACI) and multi-access interferences (MAI). State-of-the-art in this area requires complete knowledge of the MIMO transfer function which is unrealistic for practical communication systems. In MIMO systems the training sequences must also be provided by the interference-generating sources: an utterly unrealistic assumption. One of the goals of this research program is to provide more practical answers to the above problems of great practical importance by removing the need for training sequences for adaptive multichannel equalizer design. Both second-order statistics-based and higher-order statistics-based approaches are being investigated with emphasis on the former. Emphasis is on approaches that require as few assumptions as possible compared to existing literature, e.g. common zeros among the subchannels are allowed, the channel matrix impulse response can be infinitely long, etc. The results of the proposed research on blind source separation are expected to be useful to scientists and engineers engaged in processing and analysis of multisensor data in a broad class of applications such as sonar, radar, acoustic array ap plications and monitoring of power plants and civil works. The work on blind equalization is expected to result in effective and computationally efficient algorithms for signal processing in a broad class of digital communication systems such as high-speed digital subscriber lines, multi-track digital magnetic recording and multiuser wireless communications.