A Cumulant Based Subspace Method for Signal Parameter Estimation
C. Ashok, Sharon Jacob, Swetha John · 2018
The objective of this work is to estimate the Directions of Arrival (DOAs) of signals from multiple non-Gaussian sources corrupted by additive Gaussian noise impinging on a Uniform Linear Array (ULA) comprising of N sensors. This is achieved by virtue of construction of a fourth order Cumulant matrix followed by Eigen decomposition to segregate the noise and signal subspaces. Then the MUSIC algorithm is employed in order to estimate the DOAs of multiple sources. The use of higher order Statistics over its second order counterpart is found to increase the accuracy of the source localization process especially when the non-gaussian signals are corrupted by additive Gaussian noise. This approach is substantiated by means of various simulation results such as comparison of RMSE with respect to number of array elements, number of snapshots and SNR. A comparison between the second order Statistics based MUSIC and the fourth order Statistics based MUSIC algorithm is also presented in a juxtaposed manner.