{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:43:02Z","timestamp":1774629782653,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T00:00:00Z","timestamp":1620950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Synthetic aperture sonar (SAS) is a technique that acquires an underwater image by synthesizing the signal received by the sonar as it moves. By forming a synthetic aperture, the sonar overcomes physical limitations and shows superior resolution when compared with use of a side-scan sonar, which is another technique for obtaining underwater images. Conventional SAS algorithms require a high concentration of sampling in the time and space domains according to Nyquist theory. Because conventional SAS algorithms go through matched filtering, side lobes are generated, resulting in deterioration of imaging performance. To overcome the shortcomings of conventional SAS algorithms, such as the low imaging performance and the requirement for high-level sampling, this paper proposes SAS algorithms applying compressive sensing (CS). SAS imaging algorithms applying CS were formulated for a single sensor and uniform line array and were verified through simulation and experimental data. The simulation showed better resolution than the \u03c9-k algorithms, one of the representative conventional SAS algorithms, with minimal performance degradation by side lobes. The experimental data confirmed that the proposed method is superior and robust with respect to sensor loss.<\/jats:p>","DOI":"10.3390\/rs13101924","type":"journal-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T02:31:34Z","timestamp":1621218694000},"page":"1924","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Compressive Underwater Sonar Imaging with Synthetic Aperture Processing"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3465-8022","authenticated-orcid":false,"given":"Ha-min","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul 08826, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7101-5195","authenticated-orcid":false,"given":"Hae-sang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul 08826, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5393-7097","authenticated-orcid":false,"given":"Woo-jae","family":"Seong","sequence":"additional","affiliation":[{"name":"Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul 08826, Korea"},{"name":"Research Institute of Marine Systems Engineering, Seoul National University, Seoul 08826, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,14]]},"reference":[{"key":"ref_1","unstructured":"Soumekh, M. (1999). Synthetic Aperture Radar Signal. Processing with MATLAB Algorithms, Wiley-Interscience. [1st ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1109\/JOE.2009.2020853","article-title":"Synthetic Aperture Sonar: A Review of Current Status","volume":"34","author":"Michael","year":"2009","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_3","unstructured":"Hyun, A. (2016). A Study on Robust Synthetic Aperture Sonar Signal Processing for Multipath Environment using SAGE Algorithm. [Ph.D. Thesis, Seoul National University]."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gustafson, E., and Jalving, B. (2011, January 7\u20139). HUGIN 1000 Arctic Class AUV. Proceedings of the OTC Arctic Technology Conference, Houston, TX, USA.","DOI":"10.4043\/22116-MS"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kolev, N.Z. (2011). Introduction to Synthetic Aperture Sonar. Sonar Systems, IntechOpen. Chapter 1.","DOI":"10.5772\/742"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1002\/(SICI)1098-1098(1997)8:4<343::AID-IMA2>3.0.CO;2-A","article-title":"Unified Framework for Modern Synthetic Aperture Imaging Algorithms","volume":"8","author":"Gough","year":"1997","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1080\/01431168508948262","article-title":"Theory of Digital Imaging from Orbital Synthetic-Aperture Radar","volume":"6","author":"Barber","year":"1985","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1109\/36.158864","article-title":"A Comparison of Range-Doppler and Wavenumber Domain SAR Focusing Algorithms","volume":"30","author":"Bamler","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1109\/7.78293","article-title":"SAR Data Focusing using Seismic Migration Techniques","volume":"27","author":"Cafforio","year":"1991","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1190\/1.1440826","article-title":"Migration by Fourier Transform","volume":"43","author":"Stolt","year":"1978","journal-title":"Geophysics"},{"key":"ref_11","unstructured":"Cumming, I., Wong, F., and Raney, K. (1992, January 26\u201329). A SAR Processing Algorithm with No Interpolation. Proceedings of the Geoscience and Remote Sensing Symposium, Houston, TX, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Marston, T.M., Marston, P.L., and Williams, K.L. (2010, January 20\u201323). Scattering Resonances, Filtering with Reversible SAS Processing, and Applications of Quantitative Ray Theory. Proceedings of the OCEANS MTS\/IEEE, Seattle, WA, USA.","DOI":"10.1109\/OCEANS.2010.5664606"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/JOE.2016.2614717","article-title":"Wideband Synthetic Aperture Sonar Backprojection with Maximization of Wavenumber Domain Support","volume":"42","author":"Synnes","year":"2017","journal-title":"IEEE J. Oceanic Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Elad, M. (2010). Sparse and Redundant Representations: From Theory to Applications in Signal. and Image Processing, Springer. [2nd ed.].","DOI":"10.1007\/978-1-4419-7011-4"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4010","DOI":"10.1109\/TGRS.2015.2388786","article-title":"Efficient Algorithm Design for GPR Imaging of Landmines","volume":"53","author":"Krueger","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6228","DOI":"10.1109\/TGRS.2017.2723620","article-title":"Sub-Nyquist SAR via Fourier Domain Range Doppler Processing","volume":"55","author":"Aberman","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1002\/mrm.21391","article-title":"Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging","volume":"58","author":"Lustig","year":"2007","journal-title":"Magn. Reson. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MSP.2007.914728","article-title":"Compressed Sensing MRI","volume":"25","author":"Lustig","year":"2008","journal-title":"IEEE Signal. Process. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5862","DOI":"10.1109\/TIT.2010.2070191","article-title":"Toeplitz Compressed Sensing Matrices with Applications to Sparse Channel Estimation","volume":"56","author":"Haupt","year":"2010","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1109\/TSP.2011.2105480","article-title":"Innovation Rate Sampling of Pulse Streams with Application to Ultrasound Imaging","volume":"59","author":"Tur","year":"2011","journal-title":"IEEE Trans. Signal. Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1121\/1.4883360","article-title":"Compressive Beamforming","volume":"136","author":"Xenaki","year":"2014","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1121\/1.4929941","article-title":"Multiple and Single Snapshot Compressive Beamforming","volume":"138","author":"Gerstoft","year":"2015","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1109\/TCI.2016.2580498","article-title":"An Augmented Lagrangian Method for Complex-Valued Compressed SAR Imaging","volume":"2","year":"2016","journal-title":"IEEE Trans. Comput. Imag."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"G\u00fcng\u00f6r, A., \u04aaetin, M., and G\u00fcven, H.E. (2017, January 8\u201312). Autofocused Compressive SAR Imaging based on the Alternating Direction Method of Multipliers. Proceedings of the 2017 IEEE Radar Conference, Seattle, WA, USA.","DOI":"10.1109\/RADAR.2017.7944458"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4225","DOI":"10.1109\/JSEN.2017.2695001","article-title":"Single Channel SAR Deception Jamming Suppression via Dynamic Aperture Processing","volume":"17","author":"Zhao","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/MSP.2014.2312834","article-title":"Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, Autofocusing, Moving targets, and Compressed Sensing","volume":"31","author":"Varshney","year":"2014","journal-title":"IEEE Signal. Process. Mag."},{"key":"ref_27","unstructured":"Amin, M. (2014). Compressive Sensing for Urban. Radar, CRC Press. [1st ed.]."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"63","DOI":"10.2528\/PIER10080805","article-title":"Sparse Reconstruction for SAR Imaging Based on Compressed Sensing","volume":"109","author":"Wei","year":"2010","journal-title":"Prog. Electromagn. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Leier, S., and Zoubir, A.M. (2014). Aperture Undersampling using Compressive Sensing for Synthetic Aperture Stripmap Imaging. EURASIP J. Adv. Signal. Process., 156.","DOI":"10.1186\/1687-6180-2014-156"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1839","DOI":"10.1121\/1.5126862","article-title":"Compressive Synthetic Aperture Sonar Imaging with Distributed Optimization","volume":"146","author":"Xenaki","year":"2019","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ji, X., Zhou, L., and Cong, W. (2019, January 5\u20137). Effect of Incorrect Sound Velocity on Synthetic Aperture Sonar Resolution. Proceedings of the 2019 MATEC Web Conf. 2019, Sibiu, Romania.","DOI":"10.1051\/matecconf\/201928304013"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1121\/1.380678","article-title":"Comparison of Sonar System Performance Achievable using Synthetic-Aperture Techniques with the Performance Achievable by More Conventional Means","volume":"58","author":"Cutrona","year":"1975","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1121\/1.381421","article-title":"Additional Characteristics of Synthetic-Aperture Sonar Systems and a Further Comparison with Nonsynthetic-Aperture Sonar Systems","volume":"61","author":"Cutrona","year":"1977","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_34","unstructured":"Soumekh, M. (1994). Fourier Array Imaging, Prentice-Hall."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/48.557537","article-title":"Imaging Algorithms for a Strip-Map Synthetic Aperture Sonar: Minimizing the Effects of Aperture Errors and Aperture Undersampling","volume":"22","author":"Gough","year":"1997","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed Sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. Inf. Theory."},{"key":"ref_37","unstructured":"Grant, M., Boyd, S., and Ye, Y. (2020, December 01). CVX: Matlab Software for Disciplined Convex Programming. Available online: http:\/\/cvxr.com\/cvx."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.1109\/TIT.2010.2040894","article-title":"Model-Based Compressive Sensing","volume":"56","author":"Baraniuk","year":"2010","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1109\/TIT.2005.862083","article-title":"Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information","volume":"52","author":"Candes","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hor\u00e8, A., and Ziou, D. (2010, January 23\u201326). Image Quality Metrics: PSNR vs. SSIM. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.579"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Rehman, A., and Wang, Z. (2010, January 26\u201329). Reduced\u2013Reference SSIM Estimation. Proceedings of the 2010 IEEE 17th International Conference on Image Processing, Hong Kong, China.","DOI":"10.1109\/ICIP.2010.5653508"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/MSP.2007.4286571","article-title":"Compressive Sensing [Lecture Notes]","volume":"24","author":"Baraniuk","year":"2007","journal-title":"IEEE Signal. Process. Mag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s00365-007-9003-x","article-title":"A Simple Proof of the Restricted Isometry Property for Random Matrices","volume":"28","author":"Baraniuk","year":"2008","journal-title":"Constr. Approx."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/10\/1924\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:00:50Z","timestamp":1760162450000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/10\/1924"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,14]]},"references-count":43,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13101924"],"URL":"https:\/\/doi.org\/10.3390\/rs13101924","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,14]]}}}