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Found 8 papers, 0 papers with code
Wave-informed dictionary learning for high-resolution imaging in complex media
For these two steps to work together we need data from large arrays of receivers so the columns of the sensing matrix are incoherent for the first step, as well as from sub-arrays so that they are coherent enough to obtain the connectivity needed in the second step.
Synthetic aperture radar imaging below a random rough surface
Motivated by applications in unmanned aerial based ground penetrating radar for detecting buried landmines, we consider the problem of imaging small point like scatterers situated in a lossy medium below a random rough surface.
Synthetic aperture imaging of dispersive targets
The synthetic aperture imaging problem is then expanded to identify these targets and recover their locations and frequency dependent reflectivities.
Tunable high-resolution synthetic aperture radar imaging
Then we introduce a modification to Kirchhoff Migration (KM) that uses the same mechanism to produces tunable, high-resolution images.
Correlation based Imaging for rotating satellites
We call this the rank-1 image and show that it provides superior image resolution compared to the usual single-point migration scheme for fast moving and rotating objects.
Fast signal recovery from quadratic measurements
Compared to the sparse signal recovery problem that uses linear measurements, the unknown is now a matrix formed by the cross correlation of the unknown signal.
Imaging with highly incomplete and corrupted data
To improve the performance of $l_1$-minimization we propose to solve instead the augmented linear system $ [A \, | \, C] \rho =b$, where the $N \times \Sigma$ matrix $C$ is a noise collector.
The Noise Collector for sparse recovery in high dimensions
The ability to detect sparse signals from noisy high-dimensional data is a top priority in modern science and engineering.
