Synthetic Aperture Radar Back Projection Imaging Algorithm
MATLAB implementation of Synthetic Aperture Radar Back Projection imaging algorithm with detailed code structure and computational workflow
Explore MATLAB source code curated for "合成孔径雷达" with clean implementations, documentation, and examples.
MATLAB implementation of Synthetic Aperture Radar Back Projection imaging algorithm with detailed code structure and computational workflow
The Range-Doppler algorithm in synthetic aperture radar imaging, including squint angle processing scenarios, is implemented through range compression, azimuth FFT, range cell migration correction, and azimuth compression stages.
Synthetic Aperture Radar Point Target Echo Simulation MATLAB Program with Code Implementation Details and Algorithm Explanations
This MATLAB implementation provides a correlation model-based speckle reduction algorithm for Synthetic Aperture Radar (SAR) images. The program requires specifying equivalent number of looks (ENL) and filtering window size parameters to generate smoothed SAR imagery with enhanced visual quality and reduced noise interference.
Modeling of Vertical Surface Targets in Synthetic Aperture Radar (SAR) - Implementation Approaches and Algorithm Considerations
Point Target Image Processing Program for SAR Synthetic Aperture Radar Imaging Simulation
Integrated navigation program combining strapdown inertial navigation and synthetic aperture radar technologies in geographic coordinate system, with algorithm implementation details
Simulation of Synthetic Aperture Radar (SAR) using Range Doppler (RD) and Chirp Scaling (CS) imaging algorithms with implementation insights
Processing Synthetic Aperture Radar (SAR) imagery specifically designed for marine oil spill detection and analysis using advanced computational methods.
SAR Point Target Imaging Simulation with MATLAB implementations of three core algorithms (Range-Doppler, Chirp-Scaling, and Range Migration Algorithm), accompanied by detailed documentation. The Word documents provide mathematical derivations of all algorithms, combined with MATLAB imaging code for rapid SAR learning. Key technical aspects include handling two-dimensional coupling in range/azimuth directions and leveraging large time-bandwidth product signals for efficient frequency-domain processing.