Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Electrical and Computer Engineering


Alex B. Gershman


Array processing has been successfully applied in many areas such as radar, sonar and wireless communications. Most conventional array processing techniques are based on idealistic assumptions that are not valid in many practical situations. This thesis contributes to the development of novel array processing techniques for direction finding and parameter estimation in the presence of complicated spatio-temporal sources.

We address the problem of estimating the Directions-Of-Arrival (DOAs) of weak desired sources observed in the background of strong interference. We develop a new approach to beamspace preprocessing with improved robustness against out-of-sector interfering sources. Our techniques design the beamspace matrix filter based on proper tradeoffs between the in-sector (passband) source distortion and out-of-sector (stopband) source attenuation. We also introduce the novel concept of adaptive beamspace preprocessing that offers a significant improvement in the DOA estimation performance. Computationally efficient convex formulations for these beamspace matrix filter design problems are derived using second-order cone (SOC) programming.

We also develop a generalized Capon spatial spectrum estimator for localizing multiple incoherently distributed sources in sensor arrays. The proposed generalized Capon technique estimates the source central angles and angular spreads by means of a two-dimensional spectral search. The proposed method has a substantially improved performance compared to several popular spread source localization techniques.

A new search-free ESPRIT-type algorithm for estimating the DOAs of multiple chirp signals using Spatial Time-Frequency Distributions (STFDs) is developed. An averaged STFD matrix (or multiple averaged STFD matrices) is used instead of the covariance matrix to estimate the signal and noise subspaces. The proposed algorithm is shown to provide significant performance improvement over the traditional ESPRIT algorithm for FM sources, specifically in situations with closely-spaced sources and low Signal-to-Noise Ratios (SNRs).

We also develop a new algorithm for estimating the parameters of multiple wideband polynomial-phase signals (PPSs) using sensor arrays. Our approach is based on extending the high-order instantaneous moment (HIM) concept by, introducing a new nonlinear transformation called the spatial high-order instantaneous moment (SHIM). We apply this transformation to multiple wideband PPSs and employ the resulting SHIM to provide recursive estimates of the PPSs parameters. The data received at each sensor yields a different estimate of each frequency coefficient. Employing the multiple estimates simultaneously, the proposed algorithm removes the outliers and obtains a better final estimate. STFD-based methods are used in conjunction with the SHIM to estimate the DOAs of the observed signals. The proposed algorithm is shown to have an improved performance compared to the well-known chirp beam-former approach [31]. Furthermore, our algorithm is computationally more attractive as it requires multiple one-dimensional searches instead of a multi-dimensional search.

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