Tri-iterative Three-Dimensional Space-Time Adaptive Processing Based on Correlation Matrix for Airborne Radar

Because of employing elevation adaptivity, three-dimensional space-time adaptive processing (3D STAP) can achieve better performance than conventional two-dimensional (2D) STAP at the cost of higher computational load and sample support requirement. To overcome these shortcomings, a tri-iterative 3D STAP method based on correlation matrix for airborne radar is developed. In the proposed method, the quadratic cost function used in the optimum STAP is converted into three quadratic functions by using submatrices of the space-time correlation matrix, and the full dimension weight vector can be separated into three lower dimensional weight vectors. By iteratively optimizing these lower dimensional weight vectors, the proposed method can significantly decrease the computational load and training samples requirement. Experiment results using both simulated data and measured radar data demonstrate the effectiveness of the proposed method.