A novel tracking method for fast varying subspaces in impulsive noise environments

Abstract

By employing the MCC (maximum correntropy criterion) based cost function in projection approximation subspace tracking (PAST) algorithm, the MCC-PAST algorithm is deduced which can be utilized for the subspace tracking under impulsive noise environments. The Gaussian transformation technique is combined to further enhance the tracking performance. To handle the fast varying subspaces circumstances, the variable forgetting factor (VFF) technique is developed and incorporated into the algorithm. Simulation results show the robustness of the proposed nonlinear MCC-PAST with VFF algorithm, especially when the GSNR (generalized signal to noise ratio) is fairly low or the underlying noise is extremely impulsive.