Abstract:

Because of employing elevation adaptivity, three-dimensional space-time adaptive processing (3D-STAP) can achieve a better performance than conventional two-dimensional space-time adaptive processing (2D) STAP at the cost of higher computational load and sample support requirement. To overcome this shortcoming, a dimension-reduced 3D-STAP method for airborne radars based on correlation matrix is developed. In the proposed method, the quadratic cost function used in the optimum STAP is converted into two quadratic functions by using the submatrices of the space-time correlation matrix. By iteratively optimizing two lower dimensional weight vectors in two quadratic functions, 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.