Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Multiple constraint space-time direct data domain approach using nonlinear arrays
Multiple constraint space-time direct data domain approach using nonlinear arrays
Direct data domain STAP using sparse representation of clutter spectrum
Signal Processing
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In the situation that a radar platform is moving very fast, the number of training data used in space-time adaptive processing (STAP) is a major concern. Less number of training data is preferred in this situation. In this paper, four versions of statistical-based and direct data domain STAPs are discussed and compared their performance when the number of training data is varied. The four statistical-based methods are the full-rank statistical method, the relative importance of the eigenbeam (RIE) method, the principle component generalized sidelobe canceller (GSC) method, and the cross-spectral GSC method. We will compare the performance of these four methods with that of the direct data domain least squares (D3LS) approach, which utilizes only one snapshot of data in its processing. The channel mismatch will be also introduced to all methods to evaluate their performance. It is found that to make the statistical-based methods work; we need to know the rank of the interference covariance matrix. And the D3LS performs better when the number of training data available for the statistical-based methods is less than the rank of the interference covariance matrix.