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ECE DOCTOR OF PHILOSOPHY DISSERTATION DEFENSE BY: Kaushallya Adhikari

When: Tuesday, April 26, 2016
9:00 AM - 11:00 AM
Where: > See description for location
Cost: free
Description: TOPIC: PERFORMANCE ANALYSIS OF PRODUCT PROCESSING OF COLINEAR SPARSE ARRAYS

LOCATION: Lester W. Cory Conference Room, Science & Engineering Building (Group II), Room 213A

ABSTRACT:
Sampling of a propagating signal is a crucial step in radar and sonar systems. An array of sensors spatially samples a propagating signal. The most basic type of array of sensors is a uniform linear array (ULA) where the sensors are uniformly spaced on a line. The quality and the amount of information possessed by a sampled signal are highly dependent on where the signal is sampled in space and how the signals from different sensors are processed to extract the information. A beamformer processes the data sampled by an array to extract information about the signal, such as the direction of arrival for signals of interest, the number of signal sources, and the signal propagation medium.

The major theme of this thesis is the product processing of the conventional beamformer outputs of two colinear arrays that comprise a sparse non-uniform linear array. These sparse non-uniform linear arrays offer the resolution of a fully populated ULA with the same aperture using far fewer sensors. Two specific examples of product processing arrays that this thesis examines are linear coprime sensor arrays (CSAs) and nested arrays. A CSA consists of two undersampled ULAs with interleaved sensors while a nested array consists of a fully populated smaller aperture ULA nested between two adjacent sensors of an undersampled larger aperture ULA. The outputs of the product processors are spatial power spectral density estimates of the sampled signal and the bias and variance of the estimate characterize the performance of the product processors. This thesis makes three contributions to the understanding of product processing for colinear sparse arrays. First, this thesis derives the bias and variance of the product processors for sparse arrays and analyzes the results for CSAs and nested arrays. Second, this thesis provides analytical expressions for conditional probability density functions (PDFs) corresponding to signal present and absent cases in complex Gaussian signal detection by a CSA product processor and evaluates the receiver operation characteristic (ROC), facilitating the study of the detection performance. The analysis of the detection performance shows that the product processor detection gain is equal to the total number of sensors like in a linear array for medium and high signal to noise ratios. Third, this thesis determines the number of periods required in CSAs with different shadings to reduce the peak sidelobe (PSL) height to the same level as a ULA. A CSA with only one period can match the resolution of the fully populated ULA with the equivalent aperture. However, the CSA PSL height is higher than the equivalent full ULA's PSL height.

NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.

Advisor: Dr. John R. Buck
Committee Members: Dr. Paul Gendron and Dr. Karen Payton, Department of Electrical & Computer Engineering; Dr. Kathleen E. Wage, George Mason University; Dr. Piya Pal, University of Maryland

*For further information, please contact Dr. John R. Buck at 508.999.9237, or via email at jbuck@umassd.edu.
Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering