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Therefore, in this paper, we introduce the concept of multimodal data fusion from the field of artificial intelligence (AI) to the marine target detection task. Using deep learning methods, a target detection network model based on the multimodal data fusion of radar echoes is proposed. In the paper, according to the characteristics of different modalities data, the temporal LeNet (T-LeNet) network module and time-frequency feature extraction network module are constructed to extract the time domain features, frequency domain features, and time-frequency features from radar sea surface echo signals. To avoid the impact of redundant features between different modalities data on detection performance, a Self-Attention mechanism is introduced to fuse and optimize the features of different dimensions. The experimental results based on the publicly available IPIX radar and CSIR datasets show that the multimodal data fusion of radar echoes can effectively improve the detection performance of marine floating weak targets. The proposed model has a target detection probability of 0.97 when the false alarm probability is 10\u22123 under the lower signal-to-clutter ratio (SCR) sea state. Compared with the feature-based detector and the detection model based on single-modality data, the new model proposed by us has stronger detection performance and universality under various marine detection environments. Moreover, the transfer learning method is used to train the new model in this paper, which effectively reduces the model training time. This provides the possibility of applying deep learning methods to real-time target detection at sea.<\/jats:p>","DOI":"10.3390\/s22239163","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T08:13:09Z","timestamp":1669623189000},"page":"9163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Network Model for Detecting Marine Floating Weak Targets Based on Multimodal Data Fusion of Radar Echoes"],"prefix":"10.3390","volume":"22","author":[{"given":"Guoxing","family":"Duan","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4770-3753","authenticated-orcid":false,"given":"Yunhua","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"},{"name":"Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7337-9467","authenticated-orcid":false,"given":"Yanmin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}]},{"given":"Shuya","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}]},{"given":"Letian","family":"Lv","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ward, K.D., Tough, R.J.A., and Watts, S. (2013). Sea Clutter: Scattering, the K Distribution and Radar Performance, IET. [2nd ed.].","DOI":"10.1049\/PBRA025E"},{"key":"ref_2","first-page":"30","article-title":"Radar low-observable target detection","volume":"35","author":"Chen","year":"2017","journal-title":"Sci. Technol. Rev."},{"key":"ref_3","first-page":"9","article-title":"Fine processing and application of radar low observable Moving Target","volume":"35","author":"Chen","year":"2017","journal-title":"Sci. Technol. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1109\/LGRS.2013.2290024","article-title":"Detection of a Low Observable Sea-Surface Target with Micromotion via the Radon-Linear Canonical Transform","volume":"11","author":"Chen","year":"2014","journal-title":"IEEE Trans. Geosci. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Raynal, A.M., and Doerry, A.W. (2010). Doppler characteristics of sea clutter, Sandia National Laboratories (California).","DOI":"10.2172\/992329"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1109\/TGRS.2007.894442","article-title":"Statistical Properties of Low-Grazing Range-Resolved Sea Surface Backscatter Generated Through Two-Dimensional Direct Numerical Simulations","volume":"45","author":"Toporkov","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/8.655448","article-title":"Measurement and classification of low-grazing-angle radar sea spikes","volume":"46","author":"Liu","year":"1998","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_8","first-page":"18","article-title":"Adaptive Radar Signal Processing","volume":"52","author":"Roberts","year":"2010","journal-title":"Diss. Theses Gradworks"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4023405","DOI":"10.1109\/LGRS.2022.3165163","article-title":"Floating Small Target Detection in Sea Clutter Using Mean Spectral Radius","volume":"19","author":"Yan","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","first-page":"5110817","article-title":"A Method for Detecting Small Targets in Sea Surface Based on Singular Spectrum Analysis","volume":"60","author":"Wu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1109\/7.892695","article-title":"Performance analysis of two adaptive radar detectors against non-Gaussian real sea clutter data","volume":"36","author":"Gini","year":"2000","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1109\/JOE.2004.826901","article-title":"Mitigation Techniques for Non-Gaussian Sea Clutter","volume":"29","author":"Conte","year":"2004","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1109\/TAES.2002.1145752","article-title":"Vector subspace detection in compound-Gaussian clutter. Part II: Performance analysis","volume":"38","author":"Gini","year":"2002","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1109\/TAES.2010.5545205","article-title":"Impact of Sea Clutter Nonstationarity on Disturbance Covariance Matrix Estimation and CFAR Detector Performance","volume":"46","author":"Greco","year":"2010","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_15","first-page":"243","article-title":"Fractal characterisation of sea-scattered signals and detection of sea-surface targets","volume":"140","author":"Lo","year":"1993","journal-title":"Proc. Inst. Elect. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1049\/el.2014.1569","article-title":"Floating small target detection in sea clutter via normalised Hurst exponent","volume":"50","author":"Li","year":"2014","journal-title":"Electron. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/TAP.2005.861541","article-title":"Detection of low observable targets within sea clutter by structure function based multifractal analysis","volume":"54","author":"Hu","year":"2006","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1109\/TAES.2014.120657","article-title":"Tri-feature-based detection of floating small targets in sea clutter","volume":"50","author":"Shui","year":"2014","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6395","DOI":"10.1109\/TGRS.2018.2838260","article-title":"Sea-Surface Floating Small Target Detection by One-Class Classifier in Time-Frequency Feature Space","volume":"56","author":"Shi","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4947","DOI":"10.1109\/TAES.2020.3011868","article-title":"Anomaly Based Sea-Surface Small Target Detection Using K-Nearest Neighbor Classification","volume":"56","author":"Guo","year":"2020","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102707","DOI":"10.1016\/j.dsp.2020.102707","article-title":"Small Target Detection in Sea Clutter Using All-Dimensional Hurst Exponents of Complex Time Sequence","volume":"101","author":"Guo","year":"2020","journal-title":"Digit. Signal Process."},{"key":"ref_22","first-page":"684","article-title":"Status and prospects of feature-based detection methods for floating targets on the sea surface","volume":"9","author":"Xu","year":"2020","journal-title":"J. Radars"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104458","DOI":"10.1016\/j.conengprac.2020.104458","article-title":"Review on Deep Learning Techniques for Marine Object Recognition: Architectures and Algorithms","volume":"118","author":"Wang","year":"2020","journal-title":"Control Eng. Pract."},{"key":"ref_24","first-page":"105","article-title":"Radar moving target detection and classification based on time-frequency map deep learning","volume":"17","author":"Mu","year":"2019","journal-title":"J. Terahertz Electron. Inf. Technol."},{"key":"ref_25","first-page":"1987","article-title":"One-dimensional Sequence Signal Detection Method for Marine Target Based on Deep Learning","volume":"36","author":"Su","year":"2020","journal-title":"J. Signal Process."},{"key":"ref_26","first-page":"4019705","article-title":"Maritime Target Detection Based on Radar Graph Data and Graph Convolutional Network","volume":"19","author":"Su","year":"2022","journal-title":"IEEE Trans. Geosci. Res. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pan, M., Chen, J., Wang, S., and Dong, Z. (2019, January 19\u201321). A Novel Approach for Marine Small Target Detection Based on Deep Learning. Proceedings of the 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), Wuxi, China.","DOI":"10.1109\/SIPROCESS.2019.8868862"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1109\/JSTSP.2020.2987728","article-title":"Multimodal Intelligence: Representation Learning, Information Fusion, and Applications","volume":"14","author":"Zhang","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","article-title":"Multimodal Machine Learning: A Survey and Taxonomy","volume":"41","author":"Ahuja","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_32","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., \u0141ukasz, K., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the NIPS 2017\u201431st Conference on Neural Information Processing System (NIPS), Long Beach, CA, USA."},{"key":"ref_33","unstructured":"(2021, July 01). The McMaster IPIX Radar Sea Clutter Database. Available online: http:\/\/soma.ece.mcmaster.ca\/ipix\/."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/MSP.2009.935415","article-title":"Dataware: Sea clutter and small boat radar reflectivity databases [best of the web]","volume":"27","author":"Cilliers","year":"2010","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1109\/TSP.2007.909322","article-title":"Nonparametric Detection of FM Signals Using Time-Frequency Ridge Energy","volume":"56","author":"Shui","year":"2008","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Chen, K., Ding, H., and Huo, Q. (2020, January 4\u20138). Parallelizing Adam Optimizer with Blockwise Model-Update Filtering. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9052983"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lu, Z., Sun, L., and Zhou, Y. (2021). A Method for Rainfall Detection and Rainfall Intensity Level Retrieval from X-Band Marine Radar Images. Appl. Sci., 11.","DOI":"10.3390\/app11041565"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1109\/TGRS.2019.2953143","article-title":"Rain Detection From X-Band Marine Radar Images: A Support Vector Machine-Based Approach","volume":"58","author":"Chen","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9163\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:26:59Z","timestamp":1760146019000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,25]]},"references-count":39,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239163"],"URL":"https:\/\/doi.org\/10.3390\/s22239163","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,25]]}}}