High order neural network based solution for approximating the Average Likelihood Ratio

The detection of gaussian signals with unknown correlation coefficient, ρ
_s
is considered. A strategy for designing high order neural networks (HONN) in composite hypothesis test is proposed. A HONN trained with ρ
_s
varying uniformly in [0, 1] is considered to approximate the Average Likelihood Ratio (ALR). In order to compare the suitability of the approximation, a sub-optimal solution based on constrained generalized likelihood ratio is used. A study of the computational cost is carried out. Results show that a HONN is able to approximate the ALR with a low computational cost.