K-Distribution Gaussian Clutter Simulation Using Grid Mapping Method

To simulate K-distribution Gaussian clutter using MATLAB, the grid mapping method provides an effective approach. This technique involves partitioning an image into a structured grid system and performing coordinate transformations to map each grid point to corresponding positions in a rescaled grid. The implementation typically utilizes MATLAB's meshgrid function to create coordinate matrices, followed by interpolation methods (such as interp2) for precise point mapping. The K-distribution noise model is then applied to the transformed grid, which involves generating correlated Gaussian random variables and applying appropriate shape and scale parameters to achieve the desired statistical properties. This simulation method enhances realism in noise modeling by preserving spatial correlations and can be implemented using MATLAB's statistical toolbox functions like gamrnd for Gamma distribution components combined with Gaussian processes. The algorithm is particularly valuable for applications requiring accurate clutter simulation in image processing, radar signal analysis, and computer vision systems, where it helps create more authentic testing environments for signal processing algorithms. The grid mapping approach ensures consistent noise characteristics across different image resolutions while maintaining computational efficiency through vectorized operations.