Imaging Science Thesis Defense: Synthetic Aperture Radar Ground Moving Target Imaging and Motion Parameter Estimation Using Minimum Entropy Optimization
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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