Robust training samples selection algorithm based on spectral similarity for space–time adaptive processing in heterogeneous interference environments
In space-time adaptive processing (STAP), the required interference covariance matrix is commonly estimated from homogeneous samples. However, the real interference environments are often heterogeneous, which is induced by the fact that the training samples do not share the same interference property with the cell under test (CUT), and thus the interference covariance matrix estimated from the training samples is mismatched with the real one. To improve the performance of STAP in heterogeneous interference environments, this paper proposes a robust training samples selection algorithm which is based on the spectral similarity of the interference. The spectrums of samples are estimated firstly, which are then utilised to calculate the similarity between the interference of the CUT and those of the training samples. Then, the samples whose interference spectrums are similar to that of the CUT are selected as the final training samples, such that the performance of the interference covariance matrix estimated from these samples can be improved significantly. Besides, to avoid the target self-nulling effect, a simple technique is adopted to exclude the samples which are contaminated by target signal. The proposed method is applied to real radar data, and experimental results demonstrate the effectiveness of the proposed method.
