This thesis presents a fast subspace tracking method, which is called GVFF (Gradient-based Variable Forgetting Factor) FAPI, based on FAPI (Fast Approximated Power Iteration) method and GVFF-RLS (Gradient-based Variable Forgetting Factor Recursive Lea...
This thesis presents a fast subspace tracking method, which is called GVFF (Gradient-based Variable Forgetting Factor) FAPI, based on FAPI (Fast Approximated Power Iteration) method and GVFF-RLS (Gradient-based Variable Forgetting Factor Recursive Least Squares). Since the conventional FAPI uses constant forgetting factor in estimating covariance matrix of source signals, it has difficulty in applying to non-stationary environments such as continuously changing DOA of source signals. To overcome the drawback of conventional FAPI method, the GVFF FAPI introduced the gradient-based variable forgetting factor derived from an improved means square error (MSE) analysis of RLS. In order to achieve the decreased subspace error in non-stationary environments, the GVFF-FAPI algorithm used an improved forgetting factor updating equation that can produce a fast decreasing forgetting factor when the gradient is positive and a slowly increasing forgetting factor when the gradient is negative.
Our numerical simulations show that GVFF-FAPI algorithm offers lower subspace error and root mean square error (RMSE) of tracked DOA of source signals than conventional RLS based DOA tracking methods.