We develop a linearly-constrained line-search adaptive filtering algorithm by incorporating the linear constraints into the least squares problem and searching the solution (filter weights) along the Kalman gain vector. The proposed algorithm performs close to the constrained recursive least squares (CRLS) algorithm while having a computational complexity comparable to the constrained least mea...

This paper presents a novel, fully parallel algorithm for robust linearly constrained minimum variance beamforming. The robustness results from the use of an adjustable constraint to compensate for the unavoidable small mismatch between the nominal and the actual steering vector of the signal-of-interest. The need for parallelism follows from the large computational demands of beamforming algor...

This paper proposes a new adaptive algorithm for the implementation of the linearly constrained minimum variance (LCMV) beamformer. The proposed algorithm utilizes the setmembership filtering (SMF) framework and the reduced-rank joint iterative optimization (JIO) scheme. We develop a stochastic gradient (SG) based algorithm for the beamformer design. An effective time-varying bound is employed ...

This paper discusses the location bias and the spatial resolution in the reconstruction of a single dipole source by various spatial filtering techniques used for neuromagnetic imaging. We first analyze the location bias for several representative adaptive and non-adaptive spatial filters using their resolution kernels. This analysis theoretically validates previously reported empirical finding...

Optimal or unbiased estimators are widely used for state estimation and tracking. We propose a new minimum variance unbiased (MVU) finite impulse response (FIR) filter which minimizes the estimation error variance in the unbiased FIR (UFIR) filter. The relationship between the filter gains of the MVU FIR, UFIR and optimal FIR (OFIR) filters is found analytically. Simulations provided using a po...

Recently proposed dynamic magnetic resonance (MR) inverse imaging (InI) is a novel parallel imaging reconstruction technique capable of improving the temporal resolution of blood-oxygen level-dependent (BOLD) contrast functional MRI (fMRI) to the order of milliseconds at the cost of moderate spatial resolution. Volumetric InI reconstructs spatial information from projection data by solving ill-...

This paper presents a new adaptive algorithm for the linearly constrained minimum variance (LCMV) beamformer design. We incorporate the set-membership filtering (SMF) mechanism into the reduced-rank joint iterative optimization (JIO) scheme to develop a constrained recursive least squares (RLS) based algorithm called JIO-SM-RLS. The proposed algorithm inherits the positive features of reduced-r...

The weights of an optimum PB (Pre-steered broadband) antenna array processor are often obtained by solving a LCMV (linearly constrained minimum variance) problem. The objective function is the mean output power (variance) and the constraints space is a set of linear equations which ensure a constant gain in a fixed direction known as the look direction. However, errors in a practical scenario c...