{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:43:57Z","timestamp":1760060637916,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62201434","30923010933"],"award-info":[{"award-number":["62201434","30923010933"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["62201434","30923010933"],"award-info":[{"award-number":["62201434","30923010933"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Accurate sea clutter modeling is crucial for clutter suppression in edge radar processing. On resource-constrained edge radar platforms, spatiotemporal statistics, together with device-level computation and memory limits, hinder the learning of representative clutter features. This study presents a transformer-based generative adversarial model for sea clutter modeling. The core design of this work uses axial attention to factorize self-attention along pulse and range, preserving long-range dependencies under a reduced attention cost. It also introduces a two-dimensional variable-length spatiotemporal window that retains temporal and spatial coherence across observation lengths. Extensive experiments are conducted to verify the efficacy of the proposed method with quantitative criteria, including a cosine similarity score, spectral-parameter error, and amplitude\u2013distribution distances. Compared with CNN-based GAN, the proposed model achieves a high consistency with real clutter in marginal amplitude distributions, spectral characteristics, and spatiotemporal correlation patterns, while incurring a lower cost than standard multi-head self-attention. The experimental results show that the proposed method achieves improvements of 9.22% and 7.8% over the traditional AR and WaveGAN methods in terms of the similarity metric, respectively.<\/jats:p>","DOI":"10.3390\/sym17091489","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T08:40:15Z","timestamp":1757407215000},"page":"1489","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transformer-Driven GAN for High-Fidelity Edge Clutter Generation with Spatiotemporal Joint Perception"],"prefix":"10.3390","volume":"17","author":[{"given":"Xiaoya","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Junbin","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Wei","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Anqi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8650-5816","authenticated-orcid":false,"given":"Xu","family":"Liu","sequence":"additional","affiliation":[{"name":"Academy of Advanced Interdisciplinary Research, Xidian University, Xi\u2019an 710126, China"}]},{"given":"Chao","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Cheng","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8302-4435","authenticated-orcid":false,"given":"Mingliang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chongqing University, Chongqing 400044, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6429-659X","authenticated-orcid":false,"given":"Xueyong","family":"Xu","sequence":"additional","affiliation":[{"name":"North Information Control Research Academy Group Company Limited, Nanjing 211100, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1080\/17455030601097927","article-title":"Sea clutter: Scattering, the k distribution and radar performance","volume":"17","author":"Ward","year":"2007","journal-title":"Waves Random Complex Media"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106854","DOI":"10.1016\/j.engappai.2023.106854","article-title":"Spatiotemporal graph neural network for multivariate multi-step ahead time-series forecasting of sea temperature","volume":"126","author":"Kim","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.engappai.2018.01.008","article-title":"Fast selection of the sea clutter preferential distribution with neural networks","volume":"70","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/11.713055","article-title":"S-band lms propagation channel behaviour for different environments, degrees of shadowing and elevation angles","volume":"44","author":"Buonomo","year":"1998","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jie, Z., Dong, C., and Dewei, S. (2015, January 14\u201315). K distribution sea clutter modeling and simulation based on zmnl. Proceedings of the 2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA), Nanchang, China.","DOI":"10.1109\/ICICTA.2015.279"},{"key":"ref_6","unstructured":"Yi, L., Yan, L., and Han, N. (2014, January 29\u201330). Simulation of inverse gaussian compound gaussian distribution sea clutter based on sirp. Proceedings of the 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), Ottawa, ON, Canada."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8","DOI":"10.12677\/MOS.2018.71002","article-title":"Comparison and analysis of radar sea clutter k distribution sequence model simulation based on zmnl and sirp","volume":"7","author":"Ye","year":"2018","journal-title":"Model. Simul."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Guo, S., Zhang, Q., Shao, Y., and Chen, W. (2017, January 23\u201324). Sea clutter and target detection with deep neural networks. Proceedings of the 2nd International Conference on Artificial Intelligence and Engineering Applications, Guilin, China.","DOI":"10.12783\/dtcse\/aiea2017\/14949"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1109\/TBC.2019.2891051","article-title":"Implementation methodologies of deep learning-based signal detection for conventional mimo transmitters","volume":"65","author":"Baek","year":"2019","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_10","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4214","DOI":"10.1109\/TIA.2022.3160135","article-title":"Deep learning based predictive compensation of flicker, voltage dips, harmonics and interharmonics in electric arc furnaces","volume":"58","author":"Balouji","year":"2022","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TIA.2022.3231581","article-title":"Multi-source ensemble learning with acoustic spectrum analysis for fault perception of direct-buried transformer substations","volume":"59","author":"Guo","year":"2022","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yamamoto, R., Song, E., and Kim, J.-M. (2020, January 4\u20138). Parallel wavegan: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053795"},{"key":"ref_14","unstructured":"Yoon, J., Jarrett, D., and Van der Schaar, M. (2019). Time-series generative adversarial networks. Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"89","DOI":"10.5194\/isprs-archives-XLII-3-W9-89-2019","article-title":"Aenn: A generative adversarial neural network for weather radar echo extrapolation","volume":"42","author":"Jing","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Saarinen, V., and Koivunen, V. (2020, January 21\u201325). Radar waveform synthesis using generative adversarial networks. Proceedings of the 2020 IEEE Radar Conference (RadarConf20), Florence, Italy.","DOI":"10.1109\/RadarConf2043947.2020.9266709"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Truong, T., and Yanushkevich, S. (2019, January 14\u201319). Generative adversarial network for radar signal synthesis. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8851887"},{"key":"ref_18","unstructured":"Ma, X., Zhang, W., Shi, Z., and Zhao, X. (2022, January 24\u201327). Clutter simulation based on wavegan. Proceedings of the International Conference on Radar Systems, Edinburgh, UK."},{"key":"ref_19","unstructured":"Donahue, C., McAuley, J., and Puckette, M. (May, January 30). Adversarial audio synthesis. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108526","DOI":"10.1016\/j.engappai.2024.108526","article-title":"A sea\u2013land clutter classification framework for over-the-horizon radar based on weighted loss semi-supervised generative adversarial network","volume":"133","author":"Chen","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_21","first-page":"4720","article-title":"A data-enhanced high impedance fault detection method under imbalanced sample scenarios in distribution networks","volume":"59","author":"Guo","year":"2023","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_22","unstructured":"Thomas, J.A., and Cover, T.M. (2006). Elements of Information Theory, Tsinghua University Press."},{"key":"ref_23","first-page":"3","article-title":"On the estimation of the discrepancy between empirical curves of distribution for two independent samples","volume":"2","author":"Smirnoff","year":"1939","journal-title":"Bull. Math\u00e9Matique L\u2019Universit\u00e9 Mosc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1049\/ip-rsn:20010182","article-title":"Doppler modelling of radar sea clutter","volume":"148","author":"Walker","year":"2001","journal-title":"IEE Proc.-Radar Sonar Navig."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Angelliaume, S., Rosenberg, L., and Ritchie, M. (2019). Modeling the amplitude distribution of radar sea clutter. Remote Sens. Target Detect. Mar. Environ., 11.","DOI":"10.3390\/rs11030319"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"215","DOI":"10.13164\/re.2020.0215","article-title":"Mitigation of the Effects of Unknown Sea Clutter Statistics by Using Radial Basis Function Network","volume":"29","author":"Vondra","year":"2020","journal-title":"Radioengineering"},{"key":"ref_27","first-page":"250","article-title":"Sea clutter suppression and target detection algorithm of marine radar image sequence based on spatio-temporal domain joint filtering Entropy","volume":"24","author":"Wen","year":"2022","journal-title":"Signal Data Anal."},{"key":"ref_28","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 13\u201318). A simple framework for contrastive learning of visual representations. Proceedings of the International Conference on Machine Learning, Online."},{"key":"ref_29","first-page":"41","article-title":"Radar handbook","volume":"23","author":"Skolnik","year":"2008","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/TGRS.2006.888141","article-title":"Statistical analysis of high-resolution SAR ground clutter data","volume":"45","author":"Greco","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.1109\/TAES.2010.5545175","article-title":"Radar micro-Doppler signature classification using dynamic time warping","volume":"46","author":"Smith","year":"2010","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1631\/FITEE.1900523","article-title":"A convolutional neural network based approach to sea clutter suppression for small boat detection","volume":"21","author":"Li","year":"2020","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, P., Zhang, H., and Patel, V.M. (2017, January 10\u201313). Generative adversarial network-based restoration of speckled SAR images. Proceedings of the 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Cura\u00e7ao, The Netherlands.","DOI":"10.1109\/CAMSAP.2017.8313133"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3664","DOI":"10.1049\/icp.2024.1695","article-title":"Diffusion model in sea clutter simulation","volume":"Volume 2023","author":"Yang","year":"2023","journal-title":"IET Conference Proceedings CP874"},{"key":"ref_35","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A.C. (2017). Improved training of wasserstein gans. Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_36","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019, January 9\u201315). Self-attention generative adversarial networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_37","first-page":"14745","article-title":"Transgan: Two pure transformers can make one strong gan, and that can scale up","volume":"34","author":"Jiang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","unstructured":"Lee, K., Chang, H., Jiang, L., Zhang, H., Tu, Z., and Liu, C. (2021). Vitgan: Training gans with vision transformers. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, X., Metsis, V., Wang, H., and Ngu, A.H.H. (2022, January 14\u201317). Tts-gan: A transformer-based time-series generative adversarial network. Proceedings of the International Conference on Artificial Intelligence in Medicine, Halifax, NS, Canada.","DOI":"10.1007\/978-3-031-09342-5_13"},{"key":"ref_40","first-page":"1","article-title":"Bidirectional transformer gan for long-term human motion prediction","volume":"19","author":"Zhao","year":"2023","journal-title":"Acm Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Xu, L., Xu, K., Qin, Y., Li, Y., Huang, X., Lin, Z., Ye, N., and Ji, X. (2022). Tganad: Transformer-based gan for anomaly detection of time series data. Appl. Sci., 12.","DOI":"10.3390\/app12168085"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4417","DOI":"10.1002\/int.22724","article-title":"An improved gan with transformers for pedestrian trajectory prediction models","volume":"37","author":"Lv","year":"2022","journal-title":"Int. J. Intell. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1109\/7.953265","article-title":"Impact of clutter spectra on radar performance prediction","volume":"37","author":"Lombardo","year":"2001","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/18.61115","article-title":"Divergence measures based on the Shannon entropy","volume":"37","author":"Lin","year":"2002","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_45","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume":"70","author":"Arjovsky","year":"2017","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1175\/JTECH-D-13-00108.1","article-title":"Spectral Kurtosis\u2013Based Method for Weak Target Detection in Sea Clutter by Microwave Coherent Radar","volume":"32","author":"Jin","year":"2015","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_47","first-page":"1","article-title":"Sea-detecting radar experiment and target feature data acquisition for dual polarization multistate scattering dataset of marine targets","volume":"12","author":"Guan","year":"2023","journal-title":"J. Radars"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Jiang, W., Haimovich, A.M., and Simeone, O. (2019). End-to-end learning of waveform generation and detection for radar systems. arXiv.","DOI":"10.1109\/IEEECONF44664.2019.9049027"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Mateos-Ramos, J.M., Song, J., Wu, Y., H\u00e4ger, C., Keskin, M.F., Yajnanarayana, V., and Wymeersch, H. (2021). End-to-End Learning for Integrated Sensing and Communication. arXiv.","DOI":"10.1109\/ICC45855.2022.9838308"},{"key":"ref_50","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the ICML, Lille, France."},{"key":"ref_51","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_52","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016, January 5\u201310). Improved Techniques for Training GANs. Proceedings of the NeurIPS 2016, Barcelona, Spain."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T. (2020, January 13\u201319). Analyzing and Improving the Image Quality of StyleGAN. Proceedings of the CVPR 2020, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref_54","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"10850","DOI":"10.1109\/TPAMI.2023.3261988","article-title":"Diffusion Models in Vision: A Survey","volume":"45","author":"Croitoru","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1109\/TAES.2009.5089536","article-title":"Adaptive radar detection in doubly nonstationary autoregressive doppler spread clutter","volume":"45","author":"Ramakrishnan","year":"2009","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_58","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/9\/1489\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:42:20Z","timestamp":1760035340000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/9\/1489"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,9]]},"references-count":58,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["sym17091489"],"URL":"https:\/\/doi.org\/10.3390\/sym17091489","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,9,9]]}}}