Synthetic Aperture Radar (SAR) has come into widespread use in several civilian and military applications. The focus of this paper is the military application of imaging point targets captured by an airborne SAR platform. Using the traditional SAR method of determining target motion by analyzing the difference between subsequent images takes a relatively large amount of processing resources. Using methods in this thesis, target motion can be estimated before even a single image is obtained, reducing the amount of time and power used by a significantly large amount. This thesis builds on work done by Brain Zaharri and David So. Brain Zaharri successfully created a SAR simulation that accurately models the airborne SAR system capturing data of a target space using the Range Doppler Algorithm (RDA). David So extended this work by adding functionality to determine target velocity in the range and azimuth directions by processing the intermittent data created by the several steps of Brian Zaharri’s simulation. This thesis shows further extensions of processing the intermittent data using unique methods. The methods in this thesis successfully demonstrate the ability to quickly and accurately estimate target position, velocity, and acceleration without the need for using multiple SAR images. Target motion in the range direction is detected without using any part of the RDA, while the azimuth direction cuts out several steps, including the range compression phase and the range cell migration correction. Removing these unneeded processing steps dramatically decreases target motion data acquisition time. Both Brian Zaharri’s and David So’s work, along with this thesis, are part of the Cal Poly SAR Automatic Target Recognition (ATR) group of projects, which is sponsored by Raytheon Space & Airborne Systems Division. Because U.S. military SAR data remains classified, the Cal Poly SAR ATR projects addresses the need to educate researchers on the processing of SAR data.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-2601 |
| Date | 01 May 2015 |
| Creators | Kassen, Daniel S |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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