{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T22:52:48Z","timestamp":1769208768068,"version":"3.49.0"},"reference-count":60,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"61","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sci. Robot."],"published-print":{"date-parts":[[2021,12,15]]},"abstract":"<jats:p>Self-supervised training of deep neural networks in unstructured environments enables enhanced angular resolution in radar systems.<\/jats:p>","DOI":"10.1126\/scirobotics.abk0431","type":"journal-article","created":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T18:59:01Z","timestamp":1639594741000},"source":"Crossref","is-referenced-by-count":16,"title":["Coherent, super-resolved radar beamforming using self-supervised learning"],"prefix":"10.1126","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6246-085X","authenticated-orcid":true,"given":"Itai","family":"Orr","sequence":"first","affiliation":[{"name":"Faculty of Engineering and the Institute for Nanotechnology and Advanced Materials, Bar Ilan University, Ramat-Gan, Israel."},{"name":"Wisense Technologies Ltd., Tel Aviv, Israel."}]},{"given":"Moshik","family":"Cohen","sequence":"additional","affiliation":[{"name":"Wisense Technologies Ltd., Tel Aviv, Israel."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1024-3455","authenticated-orcid":true,"given":"Harel","family":"Damari","sequence":"additional","affiliation":[{"name":"Wisense Technologies Ltd., Tel Aviv, Israel."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4457-5864","authenticated-orcid":true,"given":"Meir","family":"Halachmi","sequence":"additional","affiliation":[{"name":"Wisense Technologies Ltd., Tel Aviv, Israel."}]},{"given":"Mark","family":"Raifel","sequence":"additional","affiliation":[{"name":"Wisense Technologies Ltd., Tel Aviv, Israel."}]},{"given":"Zeev","family":"Zalevsky","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and the Institute for Nanotechnology and Advanced Materials, Bar Ilan University, Ramat-Gan, Israel."}]}],"member":"221","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3141\/2606-14"},{"key":"e_1_3_2_3_2","unstructured":"Road Safety Annual Report 2019 (International Traffic Safety Data and Analysis Group 2019) pp. 1\u20139."},{"key":"e_1_3_2_4_2","unstructured":"SAE International Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles (SAE International 2018) pp. 1\u20135."},{"key":"e_1_3_2_5_2","unstructured":"International Organization for Standardization ISO 26262-1:2018 (2018); www.iso.org\/standard\/68383.html."},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3085677"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASSP.1986.1164935"},{"key":"e_1_3_2_8_2","first-page":"190","article-title":"Multiple emitter location and signal parameter estimation","author":"Schmidt R. O.","year":"1986","unstructured":"R. O. Schmidt, Multiple emitter location and signal parameter estimation. Adapt. Antennas Wirel. Commun. , 190\u2013194 (1986).","journal-title":"Adapt. Antennas Wirel. Commun."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-09380-x"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00288-6"},{"key":"e_1_3_2_11_2","first-page":"779","article-title":"Radar-based feature design and multiclass classification for road user recognition","author":"Scheiner N.","year":"2018","unstructured":"N. Scheiner, N. Appenrodt, J. DIckmann, B. Sick, Radar-based feature design and multiclass classification for road user recognition. IEEE Intell. Veh. Symp. , 779\u2013786 (2018).","journal-title":"IEEE Intell. Veh. Symp."},{"key":"e_1_3_2_12_2","doi-asserted-by":"crossref","unstructured":"K. Patel K. Rambach T. Visentin D. Rusev M. Pfeiffer B. Yang Deep learning-based object classification on automotive radar spectra in 2019 IEEE Radar Conf. RadarConf (IEEE 2019).","DOI":"10.1109\/RADAR.2019.8835775"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2967272"},{"key":"e_1_3_2_14_2","doi-asserted-by":"crossref","unstructured":"B. Major D. Fontijne R. T. Sukhavasi M. Hamilton S. Lee S. Grzechnik S. Subramanian Vehicle detection with automotive radar using deep learning on range-azimuth-doppler tensors in Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (IEEE 2019).","DOI":"10.1109\/ICCVW.2019.00121"},{"key":"e_1_3_2_15_2","unstructured":"Z. Feng S. Zhang M. Kunert W. Wiesbeck Applying neural networks with a high-resolution automotive radar for lane detection in 10th GMM-Symposium AmE 2019\u2014Automotive meets Electronics (VDE 2019)."},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","unstructured":"P. Kaul D. De Martini M. Gadd P. Newman RSS-Net: Weakly-supervised multi-class semantic segmentation with FMCW radar. arXiv:2004.03451 [cs.CV] (2 April 2020).","DOI":"10.1109\/IV47402.2020.9304674"},{"key":"e_1_3_2_17_2","doi-asserted-by":"crossref","unstructured":"O. Schumann M. Hahn J. Dickmann C. W\u00f6hler Semantic segmentation on radar point clouds. 2018 21st International Conference Information Fusion (FUSION) (IEEE 2018).","DOI":"10.23919\/ICIF.2018.8455344"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rsn.2018.5438"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2866567"},{"key":"e_1_3_2_20_2","doi-asserted-by":"crossref","unstructured":"J. Zhong G. Wen C. Ma B. Ding Radar signal reconstruction algorithm based on complex block sparse Bayesian learning in 2014 12th International Conference Signal Processing (ICSP) (IEEE 2014).","DOI":"10.1109\/ICOSP.2014.7015329"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2289875"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1186\/1687-6180-2012-44"},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","unstructured":"F. Roos H. Philipp L. Lorraine T. Torres C. Knill J. Schlichenmaier C. Vasanelli N. Appenrodt Compressed sensing based single snapshot DoA estimation for sparse MIMO radar arrays in 2019 12th German Microwave Conference (GeMiC) (IEEE 2019).","DOI":"10.23919\/GEMIC.2019.8698136"},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","unstructured":"T. Strohmer B. Friedlander Compressed sensing for MIMO radar\u2014Algorithms and performance in 2009 Conference Record of the Forty-Third Asilomar Conference on Signals Systems and Computers (IEEE 2009).","DOI":"10.1109\/ACSSC.2009.5469862"},{"key":"e_1_3_2_25_2","doi-asserted-by":"crossref","unstructured":"K. Armanious S. Abdulatif F. Aziz U. Schneider B. Yang An adversarial super-resolution remedy for radar design trade-offs in 2019 27th European Signal Processing Conference (EUSIPCO) (IEEE 2019).","DOI":"10.23919\/EUSIPCO.2019.8902510"},{"key":"e_1_3_2_26_2","unstructured":"M. Gall M. Gardill T. Horn J. Fuchs Spectrum-based single-snapshot super-resolution direction-of-arrival estimation using deep learning in 2020 German Microwave Conference (GeMiC) (IEEE 2020)."},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2019.2945115"},{"key":"e_1_3_2_28_2","doi-asserted-by":"crossref","unstructured":"M. Agatonovic Z. Stankovi\u0107 B. Milovanovi\u0107 High resolution two-dimensional DOA estimation using artificial neural networks in 2012 6th European Conference on Antennas and Propagation (EUCAP) (IEEE 2012).","DOI":"10.1109\/EuCAP.2012.6206729"},{"key":"e_1_3_2_29_2","doi-asserted-by":"crossref","unstructured":"J. Fuchs R. Weigel M. Gardill Single-snapshot direction-of-arrival estimation of multiple targets using a multi-layer perceptron in 2019 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM) (IEEE 2019).","DOI":"10.1109\/ICMIM.2019.8726554"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.2528\/PIER13012114"},{"key":"e_1_3_2_31_2","unstructured":"Y. Lecun Self Supervised Learning\u2014Keynote lecture. ICLR (2020); www.youtube.com\/watch?v=8TTK-Dd0H9U&ab_channel=AIP-PursuingSoTAAIforeveryone."},{"key":"e_1_3_2_32_2","unstructured":"T. Chen S. Kornblith M. Norouzi G. Hinton A simple framework for contrastive learning of visual representations in Proceedings of the 37th International Conference on Machine Learning (PMLR 2020)."},{"key":"e_1_3_2_33_2","unstructured":"S. Laine T. Karras J. Lehtinen T. Aila High-quality self-supervised deep image denoising in 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (ACM 2019)."},{"key":"e_1_3_2_34_2","doi-asserted-by":"crossref","unstructured":"X. Zhan Mix-and-match tuning for self-supervised semantic segmentation in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2018).","DOI":"10.1609\/aaai.v32i1.12331"},{"key":"e_1_3_2_35_2","unstructured":"S. Singh Self-supervised feature learning for semantic segmentation of overhead imagery in British Machine Vision Convention (BMVC 2018)."},{"key":"e_1_3_2_36_2","unstructured":"M. Chen T. Arti\u00e8res Unsupervised object segmentation by redrawing in 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (ACM 2019)."},{"key":"e_1_3_2_37_2","doi-asserted-by":"crossref","unstructured":"D. Dwibedi Y. Aytar J. Tompson P. Sermanet A. Zisserman G. Brain Temporal cycle-consistency learning in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE 2019).","DOI":"10.1109\/CVPR.2019.00190"},{"key":"e_1_3_2_38_2","unstructured":"E. Rodol A. Bronstein R. Kimmel Unsupervised learning of dense shape correspondence in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE 2019)."},{"key":"e_1_3_2_39_2","doi-asserted-by":"crossref","unstructured":"M. Noroozi P. Favaro Unsupervised learning of visual representations by solving jigsaw puzzles in Computer Vision \u2013 ECCV 2016. ECCV 2016. Lecture Notes in Computer Science B. Leibe J. Matas N. Sebe M. Welling Eds. (Springer 2016).","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"e_1_3_2_40_2","unstructured":"M. Janner J. Wu T. D. Kulkarni I. Yildirim J. B. Tenenbaum Self-supervised intrinsic image decomposition. arXiv:1711.03678 [cs.CV] (10 November 2017)."},{"key":"e_1_3_2_41_2","unstructured":"S. Gidaris P. Singh N. Komodakis Unsupervised representation learning by predicting image rotations. arXiv:1803.07728 [cs.CV] (21 March 2018)."},{"key":"e_1_3_2_42_2","doi-asserted-by":"crossref","unstructured":"Y. Zhang K. Li K. Li L. Wang B. Zhong Y. Fu Image super-resolution using very deep residual channel attention networks in Computer Vision \u2013 ECCV 2018. ECCV 2018. Lecture Notes in Computer Science V. Ferrari M. Hebert C. Sminchisescu Y. Weiss Eds. (Springer 2018) vol. 11211.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","unstructured":"X. Wang K. C. K. Chan C. Dong C. C. Loy EDVR: Video restoration with enhanced deformable convolutional networks in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (IEEE 2019).","DOI":"10.1109\/CVPRW.2019.00247"},{"key":"e_1_3_2_44_2","doi-asserted-by":"crossref","unstructured":"B. Lim S. Son H. Kim S. Nah K. M. Lee Enhanced deep residual networks for single image super-resolution in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (IEEE 2017).","DOI":"10.1109\/CVPRW.2017.151"},{"key":"e_1_3_2_45_2","unstructured":"C. Dong C. C. Loy Deep spatial feature transform in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE 2018)."},{"key":"e_1_3_2_46_2","unstructured":"Y.-C. Wang S. Venkataramani P. Smaragdis Self-supervised learning for speech enhancement. arXiv:2006.10388 [eess.AS] (18 June 2020)."},{"key":"e_1_3_2_47_2","doi-asserted-by":"crossref","unstructured":"B. Gfeller C. Frank D. Roblek M. Sharifi M. Tagliasacchi M. Velimirovi SPICE: Self-supervised pitch estimation in IEEE\/ACM Transactions on Audio Speech and Language Processing (IEEE 2020).","DOI":"10.1109\/TASLP.2020.2982285"},{"key":"e_1_3_2_48_2","unstructured":"J. Engel R. Swavely A. Roberts L. Hanoi H. Curtis Self-supervised pitch detection by inverse audio synthesis in ICML 2020 Workshop SAS (ICML 2020)."},{"key":"e_1_3_2_49_2","unstructured":"S. Wisdom E. Tzinis H. Erdogan R. J. Weiss K. Wilson J. R. Hershey Unsupervised speech separation using mixtures of mixtures in ICML 2020 Workshop SAS (ICML 2020)."},{"key":"e_1_3_2_50_2","doi-asserted-by":"crossref","unstructured":"A. Saeed D. Grangier N. Zeghidour Contrastive learning of general-purpose audio representations in ICASSP 2021\u20142021 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) (IEEE 2020).","DOI":"10.1109\/ICASSP39728.2021.9413528"},{"key":"e_1_3_2_51_2","doi-asserted-by":"crossref","unstructured":"M. Ravanelli Y. Bengio U. De Montr\u00e9al Learning speaker representations with mutual information. arXiv:1812.00271 [eess.AS] (1 December 2018).","DOI":"10.21437\/Interspeech.2019-2380"},{"key":"e_1_3_2_52_2","unstructured":"M. Tagliasacchi D. Roblek Self-supervised audio representation learning for mobile devices. arXiv:1905.11796 [eess.AS] (24 May 2019)."},{"key":"e_1_3_2_53_2","doi-asserted-by":"crossref","unstructured":"H. Banville I. Albuquerque A. Hyv\u00e4rinen G. Moffat D. A. Engemann A. Gramfort Self-supervised representation learning from electroencephalography signals in 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP) (IEEE 2019).","DOI":"10.1109\/MLSP.2019.8918693"},{"key":"e_1_3_2_54_2","doi-asserted-by":"crossref","unstructured":"P. Sarkar A. Etemad Self-supervised learning for ECG-based emotion recognition in ICASSP 2020\u20142020 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) (IEEE 2020).","DOI":"10.1109\/ICASSP40776.2020.9053985"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.28.1.013028"},{"key":"e_1_3_2_56_2","doi-asserted-by":"crossref","unstructured":"J. Li P. Stoica MIMO Radar Signal Processing (Wiley 2009).","DOI":"10.1002\/9780470391488"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2012.6324753"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rsn.2016.0110"},{"key":"e_1_3_2_59_2","unstructured":"O. Oktay J. Schlemper L. Le Folgoc M. Lee M. Heinrich K. Misawa K. Mori S. Mcdonagh N. Y. Hammerla B. Kainz B. Glocker D. Rueckert Attention U-Net: Learning where to look for the pancreas. arXiv:1804.03999 [cs.CV] (11 April 2018)."},{"key":"e_1_3_2_60_2","doi-asserted-by":"crossref","unstructured":"J. Fu J. Liu H. Tian Y. Li Y. Bao Z. Fang H. Lu Dual attention network for scene segmentation in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE 2019).","DOI":"10.1109\/CVPR.2019.00326"},{"key":"e_1_3_2_61_2","unstructured":"scipy.org SciPy Version: 1.7.1."}],"container-title":["Science Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.science.org\/doi\/pdf\/10.1126\/scirobotics.abk0431","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T10:38:14Z","timestamp":1705401494000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.science.org\/doi\/10.1126\/scirobotics.abk0431"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,15]]},"references-count":60,"journal-issue":{"issue":"61","published-print":{"date-parts":[[2021,12,15]]}},"alternative-id":["10.1126\/scirobotics.abk0431"],"URL":"https:\/\/doi.org\/10.1126\/scirobotics.abk0431","relation":{},"ISSN":["2470-9476"],"issn-type":[{"value":"2470-9476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,15]]},"article-number":"eabk0431"}}