Impact of Covariance Mismatched Training Samples on Constant False Alarm Rate Detectors

Olivier Besson (1)

1 ISAE-SUPAERO - Institut Supérieur de l'Aéronautique et de l'Espace

The framework of this paper is that of adaptive detection in Gaussian noise with unknown covariance matrix when the training samples do not share the same covariance matrix as the vector under test. We consider a class of constant false alarm rate detectors which depend on two statistics $(\beta,\tilde{t})$ whose distribution is parameter-free in the case of no mismatch and we analyze the impact of covariance mismatched training samples. More precisely, we provide a statistical representation of these two variables for an arbitrary mismatch. We show that covariance mismatch induces significant variations of the probability of false alarm and we investigate a way to mitigate this effect.

Mots clés: Adaptive detection; Covariance mismatch; False alarm rate

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

Olivier Besson. Impact of Covariance Mismatched Training Samples on Constant False Alarm Rate Detectors. IEEE Transactions on Signal Processing, 2021, 69, pp.755-765. ⟨10.1109/TSP.2021.3050567⟩. ⟨hal-03155122⟩