Blind Gain and Phase Calibration via Sparse Spectral Methods

Blind Gain and Phase Calibration via Sparse Spectral Methods

Blind gain and phase calibration (BGPC) is a bilinear inverse problem involving the determination of unknown gains and phases of the sensing system, and the unknown signal, jointly. BGPC arises in numerous applications, e.g., blind albedo estimation in inverse rendering, synthetic aperture radar autofocus, and sensor array auto-calibration. In some cases, sparse structure in the unknown signal ...

Sparse Grid Spectral Methods

Sparse Auto-Calibration for Radar Coincidence Imaging with Gain-Phase Errors

Radar coincidence imaging (RCI) is a high-resolution staring imaging technique without the limitation of relative motion between target and radar. The sparsity-driven approaches are commonly used in RCI, while the prior knowledge of imaging models needs to be known accurately. However, as one of the major model errors, the gain-phase error exists generally, and may cause inaccuracies of the mod...

Gain calibration methods for radio telescope arrays

In radio telescope arrays, the complex receiver gains and sensor noise powers are initially unknown and have to be calibrated. Gain calibration can enhance the quality of astronomical sky images and, moreover, improve the effectiveness of array signal processing techniques for interference mitigation and spatial filtering. A challenging aspect is that the signal-to-noise ratio (SNR) is usually ...

Sparse phase retrieval via group-sparse optimization

This paper deals with sparse phase retrieval, i.e., the problem of estimating a vector from quadratic measurements under the assumption that few components are nonzero. In particular, we consider the problem of finding the sparsest vector consistent with the measurements and reformulate it as a group-sparse optimization problem with linear constraints. Then, we analyze the convex relaxation of ...