Similarity matrix analysis and divergence measures for statistical detection of unknown deterministic signals hidden in additive noise

Abstract

International audienceThis Letter proposes an algorithm to detect an unknown deterministic signal hidden in additive white Gaussian noise. The detector is based on recurrence analysis. It compares the distribution of the similarity matrix coefficients of the measured signal with an analytic expression of the distribution expected in the noise-only case. This comparison is achieved using divergence measures. Performance analysis based on the receiver operating characteristics shows that the proposed detector outperforms the energy detector, giving a probability of detection 10% to 50% higher, and has a similar performance to that of a sub-optimal filter detector