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Atom selection strategy for signal compressed recovery based on sensing information entropy
ISA Transactions
|January 10, 2021
Summary
This study introduces an optimal atom selection strategy for signal recovery, improving performance without prior information. The new method enhances accuracy and efficiency, especially in challenging conditions like high sparsity or low signal-noise ratio.
Area of Science:
- Signal Processing
- Information Theory
- Computational Mathematics
Background:
- Atom selection is crucial for signal compressed recovery using greedy pursuit algorithms.
- Existing methods often require prior information or struggle with noisy, sparse signals.
Purpose of the Study:
- To propose an optimal atom selection strategy for signal compressed recovery that does not require prior information.
- To enhance the performance and efficiency of signal recovery algorithms.
Main Methods:
- Defined sensing information entropy to prune false atoms in the estimated support set.
- Developed a greedy pursuit algorithm with an optimal atom selection strategy.
- Validated the method through simulations and application to real-world random modulated signals.
Main Results:
- The proposed strategy significantly reduces recovery error and improves recovery probability compared to existing algorithms.
- Fewer iterations are needed, making the method more efficient.
- Demonstrated effectiveness in scenarios with high sparsity and low signal-to-noise ratio.
Conclusions:
- The novel optimal atom selection strategy enhances signal compressed recovery performance.
- The method is robust and applicable to real-world signals, showing high consistency with original data.
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