Antenna arrays that receive emissions from spatially spread sources require beamformers with wider beamwidths than point source beamformers. The framework of linearly constrained minimum variance beamforming with quadratic pattern constraints (LCMV-QPC) is used to develop beamformers with a specified main beamwidth and sidelobe levels. The problem is formulated by imposing a set of inequality c...

In this paper an inverse QR decomposition based recursive least-squares algorithm for linearly constrained minimum variance filtering is proposed. The proposed algorithm is numerically stable in finite precision environments and is suitable for implementation in systolic arrays or DSP vector architectures. Its performance is illustrated by simulations of a blind receiver for a multicarrier CDMA...

The locations of active brain areas can be estimated from the surface recordings. We describe the localization of the sources of the brain electrical activity via spatial filtering. This method incorporates the time-frequency characteristics of the neural sources for its location. The estimation of the location of active brain area is done in non-parametric fashion. The spatial filters are impl...

We introduce a new linearly constrained minimum variance (LCMV) beamformer that combines the set-membership (SM) technique with the conjugate gradient (CG) method, and develop a low-complexity adaptive filtering algorithm for beamforming. The proposed algorithm utilizes a CG-based vector and a variable forgetting factor to perform the dataselective updates that are controlled by a time-varying ...

This chapter presents reduced-rank linearly constrained minimum variance (LCMV) algorithms based on the concept of joint iterative optimization of parameters. The proposed reduced-rank scheme is based on a constrained robust joint iterative optimization (RJIO) of parameters according to the minimum variance criterion. The robust optimization procedure adjusts the parameters of a rank-reduction ...

This chapter presents reduced-rank linearly constrained minimum variance (LCMV) algorithms based on the concept of joint iterative optimization of parameters. The proposed reduced-rank scheme is based on a constrained robust joint iterative optimization (RJIO) of parameters according to the minimum variance criterion. The robust optimization procedure adjusts the parameters of a rank-reduction ...

The Kalman filter (KF) is known to loose its optimality properties when the model used does not perfectly match true system. Extending use of linear constraints this recently proved be efficient mitigate a large class parametric mismatch, through linearly constrained and minimum variance (LCKF LCMVF). However, asymptotic performances these new filters are still an open question. In work, we bri...

In this paper we propose an inverse QR decomposition based recursive least squares algorithm (IQRD-RLS) for the linearly constrained minimum variance (LCMV) receiver for CDMA transmission systems. The proposed algorithm is numerically stable in finite precision environments and it is suitable for implementation in systolic arrays or DSP vector architectures. It is shown through computer simulat...