Radar coincidence imaging (RCI) is a high‐resolution staring imaging technique without the
limitation of the target relative motion. To achieve better imaging performance, sparse …

The problem of estimating an unknown deterministic parameter vector from sign
measurements with a perturbed sensing matrix is studied in this paper. We analyze the best …

We consider the problem of finding a sparse solution for an underdetermined linear system
of equations when the known parameters on both sides of the system are subject to …

Recent developments in compressive sensing (CS) combined with increasing demands for
effective high-dimensional inference techniques across a variety of disciplines have …

Recently, compressive sensing based classification, which is called compressive
classification, has drawn a lot of attention, since it works directly in the compressive domain …

Radar coincidence imaging (RCI) is a staring imaging technique that originated from optical
coincidence imaging. In RCI, the reference matrix needs to be computed precisely to …

In remote sensing systems, exact knowledge of the sensor locations is critical for generating
focused images. In order to accurately locate misplaced or perturbed sensors from their …

The impact of random compression on the Fisher information matrix (FIM) and the Cramér-
Rao bound (CRB) is studied when estimating unknown complex parameters in the …

Compressive sensing (CS) has been widely used in vehicular technology including
compressive spectrum sensing, sparse channel estimation, and vehicular communications …

Estimates of non-random pole and residue parameters from noisy impulse-response data
are characterized. Specifically, Barankin-type lower bounds (BB) on the estimation error …