Simulation and Comparison of 3DT Dimension-Reduction Algorithm in Space-Time Adaptive Processing (STAP)

This paper presents a novel simulation program implementing the 3DT dimension-reduction algorithm for Space-Time Adaptive Processing (STAP). The MATLAB-based implementation models the core STAP methodology while incorporating dimension reduction techniques to improve computational efficiency. The simulation framework compares the algorithm's performance against optimal space-time processing benchmarks, evaluating key metrics like clutter suppression and target detection capabilities. The program structure includes modules for signal covariance matrix estimation, reduced-dimension transformation using 3DT methodology, and adaptive weight calculation. Key functions handle space-time snapshot generation, Doppler filtering, and beamforming operations. We provide detailed analysis of the algorithm's advantages in computational complexity reduction and its limitations in certain clutter environments. Furthermore, we discuss potential enhancements for better real-scenario simulation, including improved clutter modeling and non-homogeneity detection algorithms. The simulation package serves as a valuable research tool for investigating STAP methodologies, offering configurable parameters for different radar scenarios and performance validation against theoretical benchmarks.