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Date of Award

2016

Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy in Electrical Engineering (PhD)

Administrative Home Department

Department of Electrical and Computer Engineering

Advisor 1

Daniel Fuhrmann

Committee Member 1

Timothy Havens

Committee Member 2

Zhaohui Wang

Committee Member 3

Allan Struthers

Abstract

The Hybrid MIMO Phased Array Radar (HMPAR) is a multi-sensor radar architecture that merges together the concepts of a traditional phased array radar with the colocated Multiple-Input Multiple-Output (MIMO) radar. This radar system comprises a large number of transmit and receive elements, MP, organized into M sub-arrays of P elements each. The sub-arrays can be electronically steered in different directions and driven by separate transmit waveforms. Previous works focused on transmit signals strategies and beampatterns for two possible modes of operation (called Mode 1 and Mode 2). Here we concentrate on the receive signal processing algorithms and performance. Assuming that a non-moving, non-fluctuating target with an unknown complex target reflectivity is present in the field of view, we derive the Cramer-Rao Lower Bounds (CRLB), a performance bound on the variance of any unbiased target location estimator. In Mode 1, the HMPAR is used for broad beams and employs quasi-orthogonal signals. Results vary depending on the fluctuations of the beampattern. In Mode 2, the radar is used for narrower beampatterns and employs transmit signals which allow a rapid scan of the field of view in one pulse. In this case, when the sub-arrays are steered towards the true target location results show that the lowest CRLB values are obtained with low M and high P. When the HMPAR steers its beam towards the target’s presumed location, but the target is elsewhere, results vary depending on the size of the field of view. For both modes of operation, we describe potential target detection techniques, as well as providing a possible target location estimation algorithm. Specifically, by discretizing the field of view into N points, we determine the test statistic at each location and the location with the maximum value is considered the estimated target location. Afterwards, we compare the estimation algorithm performance against the CRLB. Results show that, as the SNR increases, the mean square error of the estimation algorithm reaches the performance bounds, provided by the CRLB.

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