Adaptive clutter suppression based on iterative adaptive approach for airborne radar

To improve the performance of the recently developed weighted least-squares-based iterative adaptive approach (IAA) in space–time adaptive processing (STAP) for weak or slow targets detection, we propose a novel IAA scheme to adaptively suppress the ground clutter by using the secondary training data (STD). Especially, we use the IAA to estimate the clutter plus noise covariance matrix from a very small number of STD. The resulting clutter plus noise covariance matrix can be utilized to form the STAP filter and then suppress the clutter. To reduce the computational complexity of the IAA, we exploit the sparsity of large clutter components in the angle-Doppler image and develop a modified IAA algorithm employing a soft-thresholding to adaptively determine the entries of each iteration that should be updated. Simulation results show that our proposed scheme outperforms the conventional IAA scheme over weak or slow targets detection and the modified IAA algorithm exhibits a comparable or even a better performance than the IAA algorithm but a lower computational complexity.