Proakis This is a collaborative research project with Chrysostomos L. Nikias (University of Southern California). The research focuses on the development, testing and mathematical analysis of adaptive signal processing algorithms based on nonlinear performance criteria. Areas of research include: 1. blind deconvolution algorithms based on criteria with memory nonlinearity; 2. decision feedback equalizers using higher-order statistics; 3. joint estimation and detection algorithms of the ML-type and MAP-type when the signals are Gaussian distributed; 4. adaptive multichannel signal recovery algorithms when the signals have gone through severe magnitude and phase distortion; and 5. blind deconvolution algorithms based on neural networks and Volterra filters. Special emphasis is placed on the investigation of the numerical properties and performance of the algorithms. Performance metrics being considered include probability of error in the restored sequence as a function of SNR, sensitivity to signal statistics and data conditioning, rate of convergence, choice of sliding windows and finite length effects. Applications of the new algorithms are being considered in digital communications, speech, image processing and geophysics.
