Approximate distribution of the low-rank adaptive normalized matched filter test statistic under the null hypothesis

Guillaume Ginolhac (1) , Philippe Forster (2)

1 LISTIC - Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance
2 SATIE - Systèmes et Applications des Technologies de l'Information et de l'Energie

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.

Mots clés: Adaptive detection; Low-Rank; Approximate Distribution; Perturbation Analysis; STAP

Domaines: Traitement du signal et de l'image [eess.SP]

Guillaume Ginolhac, Philippe Forster. Approximate distribution of the low-rank adaptive normalized matched filter test statistic under the null hypothesis. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52 (4), pp.2016 - 2023. ⟨10.1109/TAES.2016.150042⟩. ⟨hal-01503775⟩