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Synthesis Lectures on Signal Processing
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Published on May 2, 2019in Synthesis Lectures on Signal Processing
Henry Braun4
Estimated H-index: 4
(ASU: Arizona State University),
Pavan K. Turaga21
Estimated H-index: 21
(ASU: Arizona State University)
+ 3 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
Abstract Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors includi...
Published on Mar 3, 2009in Synthesis Lectures on Signal Processing
Danilo Orlando19
Estimated H-index: 19
,
Francesco Bandiera14
Estimated H-index: 14
,
Giuseppe Ricci27
Estimated H-index: 27
Adaptive detection of signals embedded in correlated Gaussian noise has been an active field of research in the last decades. This topic is important in many areas of signal processing such as, just to give some examples, radar, sonar, communications, and hyperspectral imaging. Most of the existing adaptive algorithms have been designed following the lead of the derivation of Kelly's detector which assumes perfect knowledge of the target steering vector. However, in realistic scenarios, mismatch...
Published on Jan 1, 2006in Synthesis Lectures on Signal Processing
Yanwei Wang8
Estimated H-index: 8
,
Jian Li73
Estimated H-index: 73
,
Petre Stoica86
Estimated H-index: 86
Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with a...
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