Support vector regression based DOA estimation in heavy tailed noise environment

In this paper, Direction Of Arrival (DOA) estimation of an incoming signal in the presence of heavy tailed noise environment is considered. Heavy tailed noise is modeled using Laplace distribution. Support vector regression (SVR) based approach is considered for DOA estimation. The computational burden of SVR due to the larger dimensionality of feature vectors/ signal vector is reduced by considering forward-backward (FB) averaging based covariance matrix of the received signal. The proposed approach is validated by several numerical results and it is found that, SVR based DOA estimation results in better performance than the MUSIC and ESPRIT in terms of estimation accuracy and computational time.