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Robust adaptive beamforming (RAB) based on interference-plus noise covariance (INC) matrix reconstruction can experience performance degradation when model mismatch errors exist, particularly when the input signal-to-noise ratio (SNR) is large. In this work, we devise an efficient RAB technique for dealing with covariance matrix

We studied the problem of sparse subspace tracking in the high-dimensional regime where the dimension is comparable to or much larger than the sample size. Leveraging power iteration and thresholding methods, a new provable algorithm called OPIT was derived for tracking the sparse principal subspace of data streams over time. We also presented a theoretical result on its convergence to verify its consistency in high dimensions. Several experiments were carried out on both synthetic and real data to demonstrate the effectiveness of OPIT.

The problem of row-sparse signal reconstruction for complex-valued data with outliers is investigated in this paper. First, we formulate the problem by taking advantage of a sparse weight matrix, which is used to down-weight the outliers. The formulated problem belongs to LASSO-type problems, and such problems can be efficiently solved via cyclic coordinate descent (CCD). We propose an extended CCD algorithm to solve the problem for complex-valued measurements, which requires careful characterization and derivation.

In this paper, we are interested in phased-array
imaging of an unknown area using narrowband RF signals and
arrays synthesized by an unmanned vehicle. Typical phased
array imaging approaches use fixed or pre-determined array
configurations for imaging, which are not usually informative
for the whole area. In this paper, we then propose an iterative
adaptive imaging approach where we identify the uncertain
regions in an initial image that need to be sensed better, find
the optimal array location and orientation for such a sensing

To solve the problem of beam selection or capturing the highest possible signal power, we propose a sequential test that can adapt to the SNR operating point and speed up the selection procedure in terms of the number of required observations in comparison to a perfectly tuned fixed length test assuming genie knowledge.

This work addresses the issue of undersampled phase retrieval using the gradient framework and proximal regularization theorem. It is formulated as an optimization problem in terms of least absolute shrinkage and selection operator (LASSO) form with (ℓ2+ℓ1) norms minimization in the case of sparse incident signals. Then, inspired by the compressive phase retrieval via majorization-minimization technique (C-PRIME) algorithm, a gradient-based PRIME algorithm is proposed to solve a quadratic approximation of the original problem.