{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:05:55Z","timestamp":1773155155649,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T00:00:00Z","timestamp":1715126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010877","name":"Science, Technology and Innovation Commission of Shenzhen Municipality","doi-asserted-by":"publisher","award":["JCYJ20210324120002007"],"award-info":[{"award-number":["JCYJ20210324120002007"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010877","name":"Science, Technology and Innovation Commission of Shenzhen Municipality","doi-asserted-by":"publisher","award":["2023B1212060024"],"award-info":[{"award-number":["2023B1212060024"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Planning Project of Key Laboratory of Advanced IntelliSense Technology, Guangdong Science and Technology Department","award":["JCYJ20210324120002007"],"award-info":[{"award-number":["JCYJ20210324120002007"]}]},{"name":"Science and Technology Planning Project of Key Laboratory of Advanced IntelliSense Technology, Guangdong Science and Technology Department","award":["2023B1212060024"],"award-info":[{"award-number":["2023B1212060024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Range-spread target (RST) detection is an important issue for high-resolution radar (HRR). Traditional detectors relying on manually designed detection statistics have their performance limitations. Therefore, in this work, two deep learning-based detectors are proposed for RST detection using HRRPs, i.e., an NLS detector and DFCW detector. The NLS detector leverages domain knowledge from the traditional detector, treating the input HRRP as a low-level feature vector for target detection. An interpretable NLS module is designed to perform noise reduction for the input HRRP. The DFCW detector takes advantage of the extracted high-level feature map of the input HRRP to improve detection performance. It incorporates a feature cross-weighting module for element-wise feature weighting within the feature map, considering the channel and spatial information jointly. Additionally, a nonlinear accumulation module is proposed to replace the conventional noncoherent accumulation operation in the double-HRRP detection scenario. Considering the influence of the target spread characteristic on detector performance, signal sparseness is introduced as a measure and used to assist in generating two datasets, i.e., a simulated dataset and measured dataset incorporating real target echoes. Experiments based on the two datasets are conducted to confirm the contribution of the designed modules to detector performance. The effectiveness of the two proposed detectors is verified through performance comparison with traditional and deep learning-based detectors.<\/jats:p>","DOI":"10.3390\/rs16101667","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T09:58:56Z","timestamp":1715162336000},"page":"1667","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Range-Spread Target Detection Networks Using HRRPs"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5898-1935","authenticated-orcid":false,"given":"Yishan","family":"Ye","sequence":"first","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"},{"name":"School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenmiao","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"},{"name":"School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pingping","family":"Pan","sequence":"additional","affiliation":[{"name":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518107, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5175-0600","authenticated-orcid":false,"given":"Wei","family":"He","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"},{"name":"School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"40301","DOI":"10.1007\/s11432-018-9811-0","article-title":"Advanced technology of high-resolution radar: Target detection, tracking, imaging, and recognition","volume":"62","author":"Long","year":"2019","journal-title":"Sci. China Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, G., Wei, Y., Ding, Z., You, P., Liu, S., and Zhang, T. (2023). Multi-Dimensional Spread Target Detection with Across Range-Doppler Unit Phenomenon Based on Generalized Radon-Fourier Transform. Remote Sens., 15.","DOI":"10.3390\/rs15082158"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1109\/TAES.1983.309368","article-title":"A High-Resolution Radar Detection Strategy","volume":"AES-19","author":"Hughes","year":"1983","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.21629\/JSEE.2019.06.07","article-title":"Design of high-performance energy integrator detector for wideband radar","volume":"30","author":"Jiayun","year":"2019","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1587\/transcom.E95.B.1475","article-title":"Improved double threshold detector for spatially distributed target","volume":"95","author":"Long","year":"2012","journal-title":"IEICE Trans. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ma, T., Gai, J., Liang, Z., Liu, Q., and Liu, H. (2021, January 17\u201320). Weighted Double Threshold Wideband Detector Based on Generalized Likelihood Ratio Test. Proceedings of the 2021 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Xi\u2019an, China.","DOI":"10.1109\/ICSPCC52875.2021.9564811"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1109\/LSP.2021.3129981","article-title":"Adaptive Double Threshold Detection Method for Range-Spread Targets","volume":"29","author":"Chen","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/97.596885","article-title":"Detection of a spatially distributed target in white noise","volume":"4","author":"Gerlach","year":"1997","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_9","first-page":"1","article-title":"Range-Spread Target Detection Based on Adaptive Scattering Centers Estimation","volume":"61","author":"Ren","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1109\/TAES.2011.5705697","article-title":"Range-Spread Target Detection using Consecutive HRRPs","volume":"47","author":"Shui","year":"2011","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1049\/iet-rsn.2010.0324","article-title":"Range-spread target detection in white Gaussian noise via two-dimensional non-linear shrinkage map and geometric average integration","volume":"6","author":"Xu","year":"2012","journal-title":"IET Radar Sonar Navig."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.sigpro.2013.12.007","article-title":"Range-spread target detection using 2D non-local nonlinear shrinkage map","volume":"98","author":"Xu","year":"2014","journal-title":"Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1080\/00207217.2018.1440436","article-title":"Range-spread target detection using the time-frequency feature based on sparse representation","volume":"105","author":"Zhang","year":"2018","journal-title":"Int. J. Electron."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103803","DOI":"10.1016\/j.dsp.2022.103803","article-title":"Detection of range-spread targets based on order statistics","volume":"133","author":"Chen","year":"2023","journal-title":"Digit. Signal Process."},{"key":"ref_15","first-page":"1","article-title":"Complex-Valued Frequency Estimation Network and Its Applications to Superresolution of Radar Range Profiles","volume":"60","author":"Pan","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.sigpro.2018.09.041","article-title":"Target-Aware Recurrent Attentional Network for Radar HRRP Target Recognition","volume":"155","author":"Xu","year":"2019","journal-title":"Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.ins.2019.06.039","article-title":"Long short-term memory-based deep recurrent neural networks for target tracking","volume":"502","author":"Gao","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_18","first-page":"1","article-title":"Deep Learning-Based UAV Detection in Pulse-Doppler Radar","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.future.2018.11.036","article-title":"Background classification method based on deep learning for intelligent automotive radar target detection","volume":"94","author":"Liu","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Diskin, T., Beer, Y., Okun, U., and Wiesel, A. (2022). CFARnet: Deep learning for target detection with constant false alarm rate. arXiv.","DOI":"10.2139\/ssrn.4590633"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, C.H., Lin, Y.C., Bai, Y., Chung, W.H., Lee, T.S., and Huttunen, H. (2019, January 22\u201325). DL-CFAR: A Novel CFAR Target Detection Method Based on Deep Learning. Proceedings of the 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), Honolulu, HI, USA.","DOI":"10.1109\/VTCFall.2019.8891420"},{"key":"ref_22","first-page":"1","article-title":"DNN-Based Peak Sequence Classification CFAR Detection Algorithm for High-Resolution FMCW Radar","volume":"60","author":"Cao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9835","DOI":"10.1007\/s00521-021-05753-w","article-title":"A method of radar target detection based on convolutional neural network","volume":"33","author":"Jiang","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_24","first-page":"1","article-title":"Range Detection on Time-Domain FMCW Radar Signals With a Deep Neural Network","volume":"5","author":"Schubert","year":"2021","journal-title":"IEEE Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jia, F., Tan, J., Lu, X., and Qian, J. (2023). Radar Timing Range\u2013Doppler Spectral Target Detection Based on Attention ConvLSTM in Traffic Scenes. Remote Sens., 15.","DOI":"10.3390\/rs15174150"},{"key":"ref_26","unstructured":"Reis, D., Kupec, J., Hong, J., and Daoudi, A. (2023). Real-Time Flying Object Detection with YOLOv8. arXiv."},{"key":"ref_27","first-page":"1","article-title":"A Study on Radar Target Detection Based on Deep Neural Networks","volume":"3","author":"Wang","year":"2019","journal-title":"IEEE Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sun, L., Liu, J., Liu, Y., and Li, B. (2021, January 14\u201317). HRRP Target Recognition Based On Soft-Boundary Deep SVDD with LSTM. Proceedings of the 2021 International Conference on Control, Automation and Information Sciences (ICCAIS), Xi\u2019an, China.","DOI":"10.1109\/ICCAIS52680.2021.9624499"},{"key":"ref_30","unstructured":"Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S.A., Binder, A., M\u00fcller, E., and Kloft, M. (2018, January 10\u201315). Deep One-Class Classification. Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9099","DOI":"10.1109\/JSEN.2021.3054744","article-title":"False-Alarm-Controllable Radar Detection for Marine Target Based on Multi Features Fusion via CNNs","volume":"21","author":"Chen","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_32","unstructured":"Wei, G., and Wu, S. (2003, January 4). Denoising radar signals using complex wavelet. Proceedings of the Seventh International Symposium on Signal Processing and Its Applications, Paris, France."},{"key":"ref_33","first-page":"1","article-title":"LBF-Based CS Algorithm for Multireceiver SAS","volume":"21","author":"Zhang","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"31957","DOI":"10.1007\/s11042-023-16757-0","article-title":"An imaging algorithm for high-resolution imaging sonar system","volume":"83","author":"Yang","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/JOE.2011.2160471","article-title":"A New Synthetic Aperture Sonar Processing Method Using Coherence Analysis","volume":"36","author":"Wachowski","year":"2011","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11454","DOI":"10.1109\/TVT.2022.3190478","article-title":"Doppler-Spread Targets Detection for FMCW Radar Using Concurrent RDMs","volume":"71","author":"Ye","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_37","unstructured":"Kay, S.M. (1998). Fundamentals of Statistical Signal Processing: Detection Theory, Volume 2, Pearson."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1667\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:42:09Z","timestamp":1760107329000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1667"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,8]]},"references-count":37,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16101667"],"URL":"https:\/\/doi.org\/10.3390\/rs16101667","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,8]]}}}