Imaging Science MS Thesis
Defense
Synthetic Aperture Radar
Ground Moving Target Imaging and Motion Parameter Estimation Using
Minimum Entropy Optimization
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Adam Cohen
Imaging Science MS
Candidate
Rochester Institute of
Technology
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 approach employs particle swarm optimization
(PSO) to iteratively minimize image entropy, estimating range velocity,
azimuth velocity, range acceleration, and azimuth acceleration while
producing focused images. It incorporates aperture lengthening to account
for target motion and a priori constraints for enhanced efficiency.
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,
wavelength, and resolutions. Results demonstrate superior accuracy, with
velocity errors below 0.02 m/s, precise azimuth acceleration 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 phase coherency 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 computationally
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.
Intended
Audience:
Beginners, undergraduates, graduates. Those with
interest in the
topic.
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