Application of Complex Empirical Mode Decomposition in separation of multiple targets using a single vector sensor

Abstract

On the basis of analysis of processing a signal from a single vector sensor using Hilbert-Huang transform (HHT) with empirical mode decomposition (EMD), complex empirical mode decomposition (CEMD) has been introduced to improve it. As an extension of EMD in complex, CEMD is a powerful tool for complex data. Its characteristic analyzing the complex white Gaussian noise has been studied. It is proved that CEMD is a dyadic filter bank and the real parts and the imaginary parts of complex IMF is with same frequency feature. Experiment has been carried with simulated signal from a single vector sensor with multiple targets, and the signals have been combined in different forms. The results show that CEMD is better in using the information between the correlative signals. Founded on different mechanism in direction estimation, it has been showed that the analytic signal is beneficial to direction estimation with different targets.