University Events

Description: Topic: Performance Prediction of Coprime Sampled Arrays in Spatially Correlated Noise

Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A

ABSTRACT:
Prior analysis on coprime sampled arrays (CSAs) made the assumption of spatially uncorrelated noise. This thesis predicts the performances of CSAs in the presence of spatially correlated noise. The noise is modeled as a first-order auto-regressive process to enable introducing inter-sensor noise correlation that decays exponentially with length. The analysis on the CSA geometry considers both the subarray product processor (CSApp) [Vaidyanathan & Pal, 2011] and the conventional beamformer (CBF) on the CSA (CSAcbf). Coarray processing allows for comparing the two CSA processors to the baseline of the densely populated CBF uniform line array (ULA). The beamformers are compared in terms of detection and estimation performance.

In terms of detection, the array gains of all processors are derived through the deflection statistic [Cox, 1973]. The deflection is generalized for processors that are either incoherent or involve non-linear processing of the array measurements. This statistic is a metric to evaluating the noise variance reduction achieved at the beamformer output by quantifying the separation of the binary hypothesis probability distribution functions (PDFs). These PDFs are used to evaluate the receiver operating characteristics of each processor to analyze and compare the beamformer detection sensitivities. In terms of estimation, the implicit Fourier relations with coarray processing and power spectral density (PSD) estimation shows the estimate produced at the beamformer output is biased. The processing of the measurements inherently smears the “true'' spatial characteristics of a random acoustic field. For the same measurements, the CSApp and CSAcbf will smear the true PSD differently. This thesis considers both aspects of performance metrics to analyze and compare the two CSA processors against the baseline of the ULA in spatially correlated noise.

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. Dayalan P. Kasilingam, Department of Electrical & Computer Engineering and Dr. Mary H. Johnson, Naval Undersea Warfare Center (NUWC)

*For further information, please contact Dr. John R. Buck at 508.999.9237, or via email at jbuck@umassd.edu.