Robust Detection Based on the K-Score Test

Abstract

This paper addresses the challenge of composite binary hypothesis testing in the presence of outliers. Within this framework, we introduce a new robust score-type detector. The proposed detector, called K-score test (K-ST), relies on an empirical version of the K-divergence that utilizes Parzen’s non-parametric "K"ernel density estimator. The use of Parzen’s density estimator provides a model-free weighting mechanism to mitigate the impact of low-density contaminations, attributed to outliers. The performance advantage of the K-ST over other robust scoretype tests, that employ model-based weighting, is demonstrated through a simulation study focusing on subspace detection.