Abstract

Approximations made in the traditional signal model for synthetic aperture radar (SAR) processing cause defocusing of the radar images when the system operates under conditions where the approximations lose validity. This dissertation investigates a number of these approximations and presents algorithmic improvements based on generalizations of the approxmations of the SAR signal model. These improvements result in better focused imagery from SAR systems with varied designs and parameters. Among the advancements presented is the development of a generalized chirp-scaling algorithm and a generalized frequency scaling algorithm to address the problems caused by approximations based on a Taylor series expansion of the SAR signal for both pulsed SAR and linear frequency modulated continuous wave (LFM-CW) SAR systems. These generalized algorithms extend the ability of frequency-domain algorithms to process SAR data from systems with a low frequency, a wide beamwidth, and a large bandwidth. Image formation algorithms are developed that account for the continuous platform motion and compensate for translational position errors due to the continuous non-ideal motion of real-world LFM-CW SAR systems, including a backprojection algorithm that does not rely upon the traditional stop-and-go approximation for platform motion.

Degree

PhD

College and Department

Ira A. Fulton College of Engineering and Technology; Electrical and Computer Engineering

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2010-03-11

Document Type

Dissertation

Handle

http://hdl.lib.byu.edu/1877/etd3439

Keywords

synthetic aperture radar, signal processing, generalization

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