Wentworth
Institute of Technology
Study on Signal Detection and Recovery Methods with Joint Sparsity
The task of signal detection is deciding whether signals of interest exist by using their observed data. Furthermore, signals are reconstructed or their key parameters are estimated from the observations in the task of signal recovery. Sparsity is a natural characteristic of most of signals in pract...
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| Format: | Electronic eBook |
| Language: | English |
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Singapore :
Springer Nature Singapore : Imprint: Springer,
2024.
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| Edition: | 1st ed. 2024. |
| Series: | Springer Theses, Recognizing Outstanding Ph.D. Research,
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| Online Access: |
Full text (Wentworth users only) |
Table of Contents:
- Introduction
- Joint Sparse Signal Detection Based On Locally Most Powerful Test Under Gaussian Model
- Joint Sparse Signal Detection Based On Locally Most Powerful Test Under Generalized Gaussian Model
- Joint Sparse Signal Recovery Based On Look-Ahead Selection of Basis-Signals
- Joint Sparse Signal Recovery Based On Two-Level Sparsity
- Summary and Outlook. .