Detection of partitioned signals by discrete cross-spectrum analysis

Partitioning of the observation space using quantiles of the underlying noise distribution is a useful technique to achieve distribution-free performance. A set of quantiles and related functions essentially determine a nonlinearity which depends on the underlying noise and precedes the standard detector. In this paper, the received data in each channel of the array is first partitioned by a set of quantiles and then transformed by a discrete Fourier transform into discrete frequency estimates which are then multiplied by complex weights. The cross-spectrum for each frequency is then obtained by the direct segment method. The results show, based on a modified version of the asymptotic relative efficiency measure, that performance gains can be achieved in non-Gaussian noise compared with detectors designed assuming Gaussian noise.