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Document Type

Open Access

Degree Program

Electrical & Computer Engineering

Degree Type

Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)

Year Degree Awarded

January 2008

Month Degree Awarded

September

Keywords

waveform design, radar target classification, digital-to-analog converter, linearization, predistortion, precompensation

Abstract

This thesis work consists of two research projects. The first project presented is on waveform design for car radars. These radars are used to detect other vehicles to avoid collision. In this project, we attempt to find the best waveform that distinguishes large objects from small ones. This helps the radar system reach more reliable decisions. We consider several models of the problem with varying complexity. For each model, we present optimization results calculated under various constraints regarding how the waveform is generated and how the reflected signal is processed. The results show that changing the radar waveform can result in better target classification.

The second project is about digital-to-analog converter (DAC) linearization. Ideally, DACs have a linear input-output relation. In practice, however, this relation is nonlinear which may be harmful for many applications. A more linear input-output relation can be achieved by modifying the input to a DAC. This method, called predistortion, requires a good understanding of how DAC errors contribute to the nonlinearity. Assuming a simple DAC model, we investigate how different error functions lead to different types of nonlinearities through theoretical analyses and supporting computer simulations. We present our results in terms of frequency spectrum calculations. We show that the nonlinearity observed at the output strongly depends on how the error is modeled. These results are helpful in designing a predistorter for linearization.

First Advisor

Dennis L. Goeckel

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