3DT Dimensionality Reduction Algorithm in Space-Time Adaptive Processing (STAP)

Space-Time Adaptive Processing (STAP) is a critical technique in radar signal processing for suppressing clutter and interference. Traditional STAP algorithms suffer from high computational complexity, making dimensionality reduction algorithms a key research focus. The 3DT (3-Dimensional Transform) method is an effective dimensionality reduction approach that employs joint space-time transformations to reduce processing dimensions while maintaining satisfactory clutter suppression performance.

The simulated 3DT dimensionality reduction algorithm in this study implements the following key steps: First, it applies a three-dimensional transformation to the received space-time radar data, mapping the original high-dimensional data to a lower-dimensional subspace using mathematical operations such as eigenvalue decomposition or principal component analysis. Then, an adaptive filter is designed on the reduced-dimension data through covariance matrix estimation and weight vector calculation, significantly improving computational efficiency. Finally, performance evaluation compares the results with optimal full-dimension STAP processing using metrics like signal-to-interference-plus-noise ratio (SINR) loss and clutter suppression capability.

Experimental results demonstrate that the 3DT algorithm achieves near-optimal STAP clutter suppression performance while substantially reducing computational complexity. This balanced performance makes it particularly suitable for real-time airborne or spaceborne radar systems. Future research could explore transform domain optimization and hybrid dimensionality reduction strategies to enhance algorithm robustness in non-homogeneous environments through techniques like iterative optimization and machine learning integration.