{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T20:03:10Z","timestamp":1783195390396,"version":"3.54.6"},"reference-count":30,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62388102"],"award-info":[{"award-number":["62388102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZR2021MF134"],"award-info":[{"award-number":["ZR2021MF134"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["62388102"],"award-info":[{"award-number":["62388102"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2021MF134"],"award-info":[{"award-number":["ZR2021MF134"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In radar High-Resolution Range Profile (HRRP) target recognition, the targets of interest are always non-cooperative, posing a significant challenge in acquiring sufficient samples. This limitation results in the prevalent issue of limited sample availability. To mitigate this problem, researchers have sought to integrate handcrafted features into deep neural networks, thereby augmenting the information content. Nevertheless, existing methodologies for fusing handcrafted and deep features often resort to simplistic addition or concatenation approaches, which fail to fully capitalize on the complementary strengths of both feature types. To address these shortcomings, this paper introduces a novel radar HRRP feature fusion technique grounded in the Feature Weight Assignment Generative Adversarial Network (FWA-GAN) framework. This method leverages the generative adversarial network architecture to facilitate feature fusion in an innovative manner. Specifically, it employs the Feature Weight Assignment Model (FWA) to adaptively assign attention weights to both handcrafted and deep features. This approach enables a more efficient utilization and seamless integration of both feature modalities, thereby enhancing the overall recognition performance under conditions of limited sample availability. As a result, the recognition rate increases by over 4% compared to other state-of-the-art methods on both the simulation and experimental datasets.<\/jats:p>","DOI":"10.3390\/rs16162963","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T11:23:46Z","timestamp":1723461826000},"page":"2963","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Limited Sample Radar HRRP Recognition Using FWA-GAN"],"prefix":"10.3390","volume":"16","author":[{"given":"Yiheng","family":"Song","sequence":"first","affiliation":[{"name":"Radar Technology Research Institute, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6688-0093","authenticated-orcid":false,"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Radar Technology Research Institute, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Zhengzhou Academy of Intelligent Technology, Beijing Institute of Technology, Zhengzhou 450000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5568-9111","authenticated-orcid":false,"given":"Yanhua","family":"Wang","sequence":"additional","affiliation":[{"name":"Radar Technology Research Institute, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Electromagnetic Sensing Research Center of CEMEE State Key Laboratory, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Beijing Key Laboratory of Embedded Real-Time Information Processing Technology, Beijing 100081, China"},{"name":"Chongqing Innovation Center, Beijing Institute of Technology, Chongqing 401120, China"},{"name":"Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/8.233138","article-title":"Using Range Profiles as Feature Vectors to Identify Aerospace Objects","volume":"41","author":"Li","year":"1993","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/62.536800","article-title":"A low-cost space-based radar system concept","volume":"11","author":"Curry","year":"1996","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_3","unstructured":"Slomka, S., Gibbins, D., Gray, D., and Haywood, B. (1999, January 22\u201325). Features for High Resolution Radar Range Profile Based Ship Classification. Proceedings of the Fifth International Symposium on Signal Processing and its Applications, Brisbane, QLD, Australia."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1117\/1.1431251","article-title":"Properties of High-resolution Range Profiles","volume":"41","author":"Xing","year":"2002","journal-title":"Opt. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1109\/TSP.2005.849161","article-title":"Radar HRRP target recognition based on higher order spectra","volume":"53","author":"Du","year":"2005","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.patrec.2012.10.010","article-title":"Radar Target Recognition Based on Fuzzy Optimal Transformation Using High-Resolution Range Profile","volume":"34","author":"Zhou","year":"2013","journal-title":"Pattern Recognit. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lei, S.Q., Yue, D.X., and Wang, F. (2021, January 11\u201316). Natural Scene Recognition Based on HRRP Statistical Modeling. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553110"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, Y., Ma, Y., Zhang, Z., Zhang, X., and Zhang, L. (2023). Type-Aspect Disentanglement Network for HRRP Target Recognition With Missing Aspects. IEEE Geosci. Remote Sens. Lett., 20.","DOI":"10.1109\/LGRS.2023.3330466"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2854","DOI":"10.1109\/TAES.2024.3353718","article-title":"A Prior-Knowledge-Guided Neural Network Based on Supervised Contrastive Learning for Radar HRRP Recognition","volume":"60","author":"Liu","year":"2024","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Wang, Y., Zhang, X., Zhang, L., and Long, T. (2024). Domain-Adaptive HRRP Generation Using Two-Stage Denoising Diffusion Probability Model. IEEE Geosci. Remote Sens. Lett., 21.","DOI":"10.1109\/LGRS.2024.3379275"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3993","DOI":"10.1109\/TAES.2024.3373378","article-title":"SDHC: Joint Semantic-Data Guided Hierarchical Classification for Fine-Grained HRRP Target Recognition","volume":"60","author":"Liu","year":"2024","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1109\/LSP.2023.3341397","article-title":"Radar-Infrared Sensor Fusion Based on Hierarchical Features Mining","volume":"31","author":"Yang","year":"2024","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5809","DOI":"10.1109\/TIP.2019.2901407","article-title":"Supervised Deep Feature Embedding with Hand Crafted Feature","volume":"28","author":"Kan","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cristianint, N. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press.","DOI":"10.1017\/CBO9780511801389"},{"key":"ref_15","unstructured":"Wei, Z., Jie, W., and Jian, G. (2011, January 26\u201330). An efficient SAR target recognition algorithm based on contour and shape context. Proceedings of the 3rd International Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Seoul, Republic of Korea."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1109\/LGRS.2012.2210385","article-title":"New discrimination features for SAR automatic target recognition","volume":"10","author":"Park","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5214313","DOI":"10.1109\/TGRS.2021.3106915","article-title":"SAR target classification using the multikernel-size feature fusion-based convolutional neural network","volume":"60","author":"Ai","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4806","DOI":"10.1109\/TGRS.2016.2551720","article-title":"Target classification using the deep convolutional networks for SAR images","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","first-page":"3504405","article-title":"One-shot HRRP generation for radar target recognition","volume":"19","author":"Shi","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2174","DOI":"10.1109\/TGRS.2020.3003264","article-title":"FEC: A feature fusion framework for SAR target recognition based on electromagnetic scattering features and deep CNN features","volume":"59","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5212614","DOI":"10.1109\/TGRS.2023.3297648","article-title":"Multifeature Collaborative Fusion Network with Deep Supervision for SAR Ship Classification","volume":"61","author":"Zheng","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","first-page":"5227914","article-title":"Multilevel Scattering Center and Deep Feature Fusion Learning Framework for SAR Target Recognition","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, Y., Du, L., and Wei, D. (2024). Multiscale CNN Based on Component Analysis for SAR ATR. IEEE Trans. Geosci. Remote Sens., 60.","DOI":"10.1109\/TGRS.2021.3100137"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4182","DOI":"10.1109\/TAES.2024.3373379","article-title":"Scattering Attribute Embedded Network for Few-Shot SAR ATR","volume":"60","author":"Qin","year":"2024","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_25","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet classifica tion with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Stateline, NV, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yadav, D., Kohli, N., and Agarwal, A. (2018, January 18\u201322). Fusion of Handcrafted and Deep Learning Features for Large-scale Multiple Iris Presentation Attack Detection. Proceedings of the International Conference on Computer Vision and Pattern Recognition-Workshop on Biometrics (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00099"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1049\/ell2.12225","article-title":"Polarimetric HRRP recognition based on feature-guided transformer model","volume":"57","author":"Zhang","year":"2021","journal-title":"Electron. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wan, J., Chen, B., Xu, B., Liu, H., and Jin, L. (2019). Convolutional neuralnetworks for radar HRRP target recognition and rejection. EURASIP J. Adv. Signal Process., 2019.","DOI":"10.1186\/s13634-019-0603-y"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1109\/LGRS.2018.2865608","article-title":"Multiple feature aggregation using convolutional neural networks for SAR image-based automatic target recognition","volume":"15","author":"Cho","year":"2018","journal-title":"lEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1016\/S0031-3203(02)00262-5","article-title":"Feature fusion: Parallel strategy vs. serial strategy","volume":"36","author":"Yang","year":"2003","journal-title":"Pattern Recognit."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/2963\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:35:36Z","timestamp":1760110536000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/2963"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,12]]},"references-count":30,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["rs16162963"],"URL":"https:\/\/doi.org\/10.3390\/rs16162963","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,12]]}}}