Full polarisation ISAR imaging based on joint sparse Bayesian compressive sensing
| Autor: | Chen Rushan, Wang Xin, Chunying Pei, Gu Yalong, Shifei Tao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Synthetic aperture radar
Computer science Energy Engineering and Power Technology electromagnetic wave polarisation joint sparse algorithm bayes methods Bayesian compressive sensing Radar imaging single channel polarisation imaging Computer vision polarisation inverse synthetic aperture radar imaging performance amplitude information correlation joint sparse bayesian compressive sensing Joint (geology) compressed sensing business.industry General Engineering full polarisation isar imaging Inverse synthetic aperture radar radar imaging Compressed sensing lcsh:TA1-2040 Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) Software synthetic aperture radar |
| Zdroj: | The Journal of Engineering (2019) |
| DOI: | 10.1049/joe.2019.0365 |
| Popis: | This study proposes a joint sparse algorithm based on Bayesian compressive sensing to improve full polarisation inverse synthetic aperture radar (ISAR) imaging performance. The proposed method not only uses the sparseness of each single channel polarisation, but also takes into account the correlation of amplitude information between single-polarised channels. Through the comprehensive use of single channel polarisation imaging results, a better full-polarisation imaging result is achieved. Simulation results are used to verify the effectiveness of the method. |
| Databáze: | OpenAIRE |
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