Shrinkage widely linear recursive least squares (SWL-RLS) and its improved version called structured shrinkage widely linear recursive least squares (SSWL-RLS) algorithms are proposed in this paper. By using the relationship between the noise-free a posterior and a priori error signals, the optimal forgetting factor can be obtained at each snapshot. In the implementation of algorithms, due to the a priori error signal known, we still need the information about the noise-free a priori error which can be estimated with a known formula. Simulation results illustrate that the proposed algorithms have faster convergence and better tracking capability than augmented RLS (A-RLS), augmented least mean square (A-LMS) and SWL-LMS algorithms.

Ryu et al.'s recent paper proposed a multiple target angle-tracking algorithm without data association. This algorithm, however, shows degraded performance on evasive maneuvering targets, because the estimated signal subspace is degraded in the algorithm. In this paper, we propose a new algorithm where, VFF-PASTd (Variable Forgetting Factor PASTd) algorithm is applied to the Ryu's algorithm to effectively handle the evasive target tracking with better time-varying signal subspace.

Wireless LAN (WLAN) systems transmit and receive via a common frequency band. In this band, signals of other wireless applications operate on a WLAN beamformer as interferences, and so the problem in adaptive antenna is increasing the canceling performance in the presence of moving interference sources. The performance of conventional adaptive beamformer is severely degraded and the robust adaptive beamformer must be equipped with additional sensors to obtain desired performances. Therefore, in order to avoid having to install additional sensors, an efficient algorithm is necessary. In this paper, we introduce a fast adaptive algorithm with variable forgetting factor, which does not require any further additional modifications. Through computer simulations, we can obtain better performances than those of other techniques under a variety of operating conditions.