Approximate distribution of the low-rank adaptive normalized matched filter test statistic under the null hypothesis
Résumé
In this paper, we propose to derive an approximate theoretical distribution under the null hypothesis of the Low-Rank Adaptive Normalized Matched Filter (LR-ANMF). This detector is used to detect a target when the disturbance is composed of a Low-Rank Gaussian contribution (called clutter) and an Additive White Gaussian Noise (AWGN). In the LR-ANMF, the estimated covariance matrix is replaced by the estimated orthogonal projector onto the subspace clutter. The method to derive this distribution is based on a perturbation analysis and assumes that the steering vector is far from the clutter and a large Clutter-to-Noise Ratio (CNR). Simulations on a STAP example validate our theoretical result and the impact of both hypotheses is also studied.
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