Superresolution inverse synthetic aperture radar (ISAR) imaging using compressive sampling

Suman K. Gunnala; Saibun Tjuatja

doi:10.1117/12.850225

18 April 2010 Superresolution inverse synthetic aperture radar (ISAR) imaging using compressive sampling

Suman K. Gunnala, Saibun Tjuatja

Proceedings Volume 7699, Algorithms for Synthetic Aperture Radar Imagery XVII; 76990A (2010) https://doi.org/10.1117/12.850225
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

A method based on compressive sampling to achieve superresolution in ISAR imaging is presented. The superresolution ISAR imaging algorithm is implemented by enforcing the sparsity constraints via random compressive sampling of the measured data. Sparsity constraint ratio (SCR) is used as a design parameter. Mutual coherence is used as a quantitative measure to determine the optimal SCR. ISAR data for full angular sector as well as different partial angular sectors are utilized in this study. Results show that significant resolution enhancement is achieved around optimal SCR of 0.2.

Citation Download Citation

Suman K. Gunnala and Saibun Tjuatja "Superresolution inverse synthetic aperture radar (ISAR) imaging using compressive sampling", Proc. SPIE 7699, Algorithms for Synthetic Aperture Radar Imagery XVII, 76990A (18 April 2010); https://doi.org/10.1117/12.850225

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