Direction finding of maximum likelihood algorithm using quantum bee colony for noncircular signals

Abstract

The noncircular maximum likelihood (NC-ML) algorithm proposed in this paper is an extension of the maximum likelihood (ML) algorithm to the case of noncircular signals which are used in communication systems. Due to the utilization of noncircular information of signals, the root mean square error (RMSE) performance of NC-ML is better than ML algorithm for noncircular signals. The proposed NC-ML algorithm utilized few virtual elements and expanded the number of effective aperture array, while significantly improving the performance of the original ML algorithms. In order to fit the proposed NC-ML algorithm, a quantum bee colony (QBC) algorithm is proposed and applied to objective function of NC-ML. The quantum bee colony is a novel intelligence algorithm for optimization problem, which can solve NC-ML with fast velocity. Monte-Carlo simulations have proved that the proposed NC-ML method based on the QBC has good performance for noncoherent and coherent signals.