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Compressive sensing for inverse synthetic aperture radar imaging
Alessio Bacci
, Elisa Giusti
, Sonia Tomei
, Davide Cataldo
,
Marco Martorella
, Fabrizio Berizzi
Engineering
Research output
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Keyphrases
Imaging Methods
100%
High-resolution Image
100%
Super-resolution Algorithm
100%
Inverse Synthetic Aperture Radar Imaging
100%
Compressed Sensing
100%
Doppler Method
100%
Automatic Target Recognition
100%
Range-Doppler
100%
Fourier Imaging
100%
Processing Time
50%
Increased Demand
50%
Large Bandwidth
50%
Slow Time
50%
Fine Resolution
50%
Coarse Resolution
50%
Apodization
50%
Data Compression
50%
Computationally Expensive
50%
Coherent Processing
50%
Homeland Security
50%
Aspect Angle
50%
Extrapolation Method
50%
Surveillance Security
50%
Cross Range
50%
Missing Samples
50%
Radar Imaging Techniques
50%
Bandwidth Extrapolation
50%
Coherent Integration
50%
Engineering
High Resolution
100%
Image Analysis
100%
Inverse Synthetic Aperture Radar
100%
Resolution Image
100%
Compressive Sensing
100%
Processing Time
50%
Time Domain
50%
Disruptions
50%
Moving Target
50%
Increasing Demand
50%
Main Disadvantage
50%
Aspect Angle
50%
Signal Bandwidth
50%
Cross-Range
50%
Fine Resolution
50%
Automatic Target Recognition
50%
Apodization
50%
Conventional Case
50%
Computer Science
Image Analysis
100%
Resolution Image
100%
super resolution
100%
Compressive Sensing
100%
Inverse Synthetic Aperture Radar
100%
Processing Time
50%
Research Community
50%
Homeland Security
50%
Data Compression
50%
Resolution Method
50%
Automatic Target Recognition
50%
Signal Bandwidth
50%
Physics
High Resolution
100%
Image Analysis
100%
Synthetic Aperture Radar
100%
Imaging Technique
50%
Data Compression
50%
Automatic Target Recognition
50%
Apodization
50%