The post-Doppler adaptive processing method based on the spatial domain reconstruction
Space-time adaptive processing (STAP) has a huge computational complexity and a large training samples requirement, which limit its practical applications. The traditional post-Doppler adaptive processing methods such as factored approach (FA) and extended factored approach (EFA) can significantly reduce the computational complexity and the training sample requirement in adaptive processing, and maintain nearly the same performance as the optimal STAP. However, because training samples are restricted in real-world environments, their performances can be considerably degraded in the large-scale antenna array. To solve this problem, the post-Doppler adaptive processing method based on the spatial domain reconstruction is proposed. In this method, the spatial clutter data after Doppler filtering is reconstructed as a matrix that has close columns and rows. The spatial weights vector in FA or EFA is also re-expressed as the product of two shorter weight vectors. Then the cyclic minimizer is applied to find the desired solution. Experimental results show that the proposed method has the advantages of fast convergence and small training samples requirement. It has greater moving target detection ability especially under the condition for large-scale antenna array and small training samples support than FA and EFA.
space-time adaptive processing (stap) computational complexity convergence clutter suppression