Direction-of-arrival (DOA) estimation based on subspace methods has collected much interest over a few decades, and adaptive DOA estimation with rapidly changing parameters will be necessary for wireless communications. This paper is concerned with a new subspace tracking scheme by using an accelerated LMS and RLS algorithms for time-varying parameters. The proposed accelerated adaptive algorithms are based on the internal model principle by approximately expressing the changing parameters by an expansion of polynomial time functions. Thus its application to DOA estimation based on the MUSIC and MODE schemes is presented and the effectiveness is validated in numerical simulations.

This paper introduces some modelling methods of time-varying stochastic process and its linear/nonlinear adaptive identification. Time-varying models are often identified by using a least square criterion. However the criterion should assume a time invariant stochastic model and infinite observed data. In order to adjust these serious different assumptions, some windowing techniques are introduced. Although the windows are usually applied to a batch processing of parameter estimates, all adaptive methods should also consider them at difference point of view. In this paper, two typical windowing techniques are explained into adaptive processing. In addition to the use of windows, time-varying stochastic ARMA models are built with these criterions and windows. By using these criterions and models, this paper explains nonlinear parameter estimation and the property of estimation convergence. On these discussions, some approaches are introduced, i.e., sophisticated stochastic modelling and multi-rate processing.