Abstract

Synthetic aperture radar (SAR) imaging of ground moving targets presents significant challenges due to motion-induced defocus and displacement in single-channel stripmap mode data.  This thesis introduces a novel algorithm for ground moving target imaging (GMTIm) and motion  parameter estimation using minimum entropy optimization. The algorithm begins by using a technique  called aperture lengthening to determine the target’s azimuth velocity (va). Next, particle swarm  optimization (PSO) iteratively minimizes image entropy by varying range velocity (vr ), range  acceleration (ar ), and azimuth acceleration (aa) to produce focused images. The algorithm outputs  the final focused image and the set of motion parameters used to form it. Building on SAR fundamentals and the signal model for moving targets, the proposed method addresses  limitations in existing algorithms, such as the Keystone Transform with Fractional Fourier  Transform (KT-FrFT) and Hough Transform with Polynomial Fourier Transform (HT-PFT). Simulations of  point targets varied parameters including motion, signal- to-noise ratio (SNR down to 13 dB), range  to ground reference point, sampling rates, wave- length, and resolutions. Results demonstrate  superior accuracy, with vr errors below 0.02 m/s, precise aa recovery (unachievable by benchmarks),  and impulse response metrics like peak sidelobe ratio (PSLR) nearing -13 dB and integrated sidelobe  ratio (ISLR) around -10 dB. Monte-Carlo analysis confirmed low variance and reliable convergence. For extended targets, such as simulated tanks, qualitative imaging and interferometric SAR (InSAR)  analysis revealed phase errors under 0.05 m, outperforming benchmarks in fidelity and detail  preservation. This work fills a gap in single-channel GMTIm by providing a com- putationally  feasible, optimization-driven solution adaptable to various SAR configurations, with applications  in defense, remote sensing, and automatic target recognition (ATR). Future extensions include multi-static setups and real-data validation.

Publication Date

6-30-2026

Document Type

Thesis

Student Type

Graduate

Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science

College

College of Science

Advisor

Charles Bachmann,

Advisor/Committee Member

James Albano

Advisor/Committee Member

John Kerekes

Comments

This thesis has been embargoed. The full-text will be available on or around 5/12/2027.

Campus

RIT – Main Campus

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