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In this paper, we develop verifiable and computable performance analysis of the ℓ∞ norms of the recovery errors for ℓ1 minimization algorithms. We define a family of goodness measures for arbitrary sensing matrices as a set of optimization problems, and design algorithms with a theoretical global convergence guarantee to compute these goodness measures. The proposed algorithms solve a series of second-order...
Distributed Multiple Input Multiple Output (MIMO) radar systems enable viewing the targets from different angles, thereby providing spatial diversity gain. In this paper, we propose an approach to accurately estimate the parameters (position, velocity) of multiple targets using such systems from fewer number of samples by employing compressive sensing. We also introduce a new metric to analyze the...
We develop a near-field compressive sensing (CS) estimation scheme for localizing scattering objects in vacuum. The potential of CS for localizing sparse targets was demonstrated in previous work. We extend the standard far-field approach to near-field scenarios by employing the electric field integral equation to capture the mutual interference among targets. We show that the advanced modeling improves...
The performance of estimating the common support for jointly sparse signals based on their projections onto lower-dimensional space is analyzed. Support recovery is formulated as a multiple-hypothesis testing problem. Both upper and lower bounds on the probability of error are derived for general measurement matrices, by using the Chernoff bound and Fano's inequality, respectively. The upper bound...
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