The space-time adaptive processing(STAP) technique adaptively suppresses clutter jointly in the space domain and time domain, but in practice, due to the lack of sample quantity, the performance of this technique suffers a serious loss. In contrast, the knowledge-aided space-time adaptive processing(KA-STAP) algorithm can improve the estimation accuracy of the clutter covariance matrix by using a priori knowledge. In this paper, by using the cycle characteristic of the clutter covariance matrix as the priori knowledge, the spatial circular matrix, the temporal circular matrix and the spatial-temporal circular matrix are constructed. And use the Least-Mean-Square(LMS) criterion to calculate the coefficients and then integrate the above matrices. Compared with the traditional STAP method, the KA-STAP using the cyclic property as a priori knowledge has about 1 dB of output signal-to-clutter-noise(SCNR) improvement.

Sparse recovery space-time adaptive processing requires the use of dictionaries composed of redundant basis vectors, but sometimes the dictionary division is inaccurate. At this time, the off network effect will occur..Therefore, this paper studies the elimination of off-grid effect by two-dimensional iterative adaptive dictionary correction algorithm. In this method, the covariance matrix of clutter plus noise is estimated by the 2D IAA-STAP algorithm, and then the atoms most relevant to clutter points are obtained. The maximum joint likelihood function is used to search the clutter Doppler frequency atoms in the local area. Finally, a new space-time oriented dictionary is formed by iteration. The experimental simulation shows that the proposed method can make the clutter points attach to the clutter ridge better and reduce the off-grid effect more effectively.

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