Abstract
Subspace space–time adaptive processing (STAP) algorithms are able to eliminate clutter completely. However, when the number of training samples is smaller than the clutter rank, the performances of subspace STAP algorithms degrade severely due to the inaccurate estimate of clutter subspace. To remedy this problem, a novel subspace STAP algorithm is proposed. In the proposed algorithm, the entire clutter subspace is constructed by two portions. The direct portion is estimated by a conventional method from limited training samples, while the supplemented portion is constructed by some space–time steering vectors selected from an overcomplete space–time steering dictionary. Clutter suppression is achieved by projecting the data into the subspace orthogonal to the clutter subspace. Numerical results with both simulated and mountain-top data demonstrate that the proposed algorithm has superior performance in a finite-training-sample situation.
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