Identifiability in Bilinear Inverse Problems With Applications to Subspace or Sparsity-Constrained Blind Gain and Phase Calibration

Bilinear inverse problems (BIPs), the resolution of two vectors given their image under a bilinear mapping, arise in many applications. Without further constraints, BIPs are usually ill-posed. In practice, the properties of natural signals are exploited to solve BIPs. For example, subspace constrain...

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Veröffentlicht in:	IEEE transactions on information theory Jg. 63; H. 2; S. 822 - 842
Hauptverfasser:	Yanjun Li, Kiryung Lee, Bresler, Yoram
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.02.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Albedo Ambiguity blind gain and phase calibration Calibration Deconvolution Discrete Fourier transforms equivalence class Ill posed problems Information theory Inverse problems inverse rendering Lower bounds Mapping Mathematical models multichannel blind deconvolution Rendering (computer graphics) SAR autofocus sensor array processing Sensor arrays Sparse matrices Sparsity Subspace constraints Subspaces Tightness transformation group Transformations Uniqueness Vector space
ISSN:	0018-9448, 1557-9654
Online-Zugang:	Volltext
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