Reduced-dimension (RD) space-time adaptive processing (STAP) technique has achieved good clutter suppression performance in real data processing. However, the traditional F$A method suffers from performance degradation in presence of clutter fluctuation. Extended F$A method (E-F$A) is an effective approach to improve the performance in such a clutter environment, but it requires high computational complexity and sample support. In this paper, a novel reduced-dimension method for E-F$A clutter suppression is proposed. Firstly, we extract characteristics of clutter by tensor Tucker decomposition, which can preserve the structural characteristics of the clutter in the spatial and Doppler domains. Then, we select eigenvectors to construct the RD matrix based on principle components (PC) analysis. Finally, RD data is obtained by multiplying the RD matrix with the original data, and the weight vector for clutter suppression can be calculated. The experimental results based on real measured data validate the effectiveness of the proposed method.
Space-time adaptive processing (STAP) can effectively suppress the clutter, which plays an important role in ground moving target indication (GMTI). However, it is difficult to obtain sufficient training samples with an increase in the number of spatial channels and adaptive processor dimensions in large arrays, especially in a complex geomagnetic detection environment. Traditional reduced-dimension STAP methods cannot offer significant benefits in real data processing in this issue. Thus, in this paper, a reduced-dimension post-Doppler STAP method based on tensor Tucker decomposition is proposed. Firstly, the distribution characteristics of the clutter spectrum in the post-Doppler domain are analyzed. Then, the feature spaces of beam and Doppler are extracted by tensor Tucker decomposition. Finally, the data dimension is reduced by the feature spaces, and clutter suppression is carried out. The results of the experiments based on real measured data demonstrate that the proposed method can achieve good performance with fewer samples than traditional methods.
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