ROBUST AND ITERATIVE ADAPTIVE SIGNAL PROCESSING By LIN DU A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ROBUST AND ITERATIVE ADAPTIVE SIGNAL PROCESSING By Lin Du May 2010 Chair: Jian Li Major: Electrical and Computer Engineering Adaptive signal processing plays an important role in many applications including radar, sonar, acoustics, communications, image processing, speech processing, medical imaging and other fields. The goal of this dissertation is to investigate several adaptive signal processing techniques and their related applications. We focus on adaptive beamforming, ultrasound imaging, Doppler spectrogram analysis and aeroacoustic noise analysis. We first consider the adaptive signal processing techniques in the context of beamforming. Numerous approaches have been proposed in the literature to improve the robustness of the data-adaptive standard Capon beamformer (SCB). One of the most popular and widely used robust adaptive beamforming methods is the diagonal loading approach (as well as its extended versions). However, most of these schemes determine the diagonal loading level either in an ad-hoc way or need user parameters that might be hard to determine in practice. Therefore, user parameter-free approaches are desirable. We present a fully automatic approach to compute the diagonal loading level. In our diagonal loading algorithm, the conventional sample covariance matrix used in the SCB formulation is replaced by an enhanced covariance matrix estimate based on a shrinkage method. The enhanced estimate can be achieved by a general linear combination (GLC), of the sample covariance matrix and an identity matrix in a minimum mean-squared error (MSE) sense. The shrinkage parameters, which are related to the