Submit Manuscript
Peer Review
Editor Work
| Citation: | GUO Shuai, CHEN Ting, WANG Penghui, et al. Multistation cooperative radar target recognition based on an angle-guided transformer fusion network[J]. Journal of Radars, 2023, 12(3): 516–528. doi: 10.12000/JR23014
|
Multistation Cooperative Radar Target Recognition Based on an Angle-guided Transformer Fusion Network
doi: 10.12000/JR23014
More Information-
Abstract
Multistation cooperative radar target recognition aims to enhance recognition performance by utilizing the complementarity between multistation information. Conventional multistation cooperative target recognition methods do not explicitly consider the issue of interstation data differences and typically adopt relatively simple fusion strategies, which makes it difficult to obtain accurate and robust recognition performance. In this study, we propose an angle-guided transformer fusion network for multistation radar High-Resolution Range Profile (HRRP) target recognition. The extraction of the local and global features of the single-station HRRP is conducted via feature extraction, which employs a transformer as its main structure. Furthermore, three new auxiliary modules are created to facilitate fusion learning: the angle-guided module, the prefeature interaction module, and the deep attention feature fusion module. First, the angle guidance module enhances the robustness and consistency of features via modeling data differences between multiple stations and reinforces individual features associated with the observation perspective. Second, the fusion approach is optimized, and the multilevel hierarchical fusion of multistation features is achieved by combining the prefeature interaction module and the deep attention feature fusion module. Finally, the experiments are conducted on the basis of the simulated multistation scenarios with measured data, and the outcomes demonstrate that our approach can effectively enhance the performance of target recognition in multistation coordination. -
-
References
[1] DING Beicheng and CHEN Penghui. HRRP feature extraction and recognition method of radar ground target using convolutional neural network[C]. 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA), Granada, Spain, 2019: 658–661.[2] WAN Jinwei, CHEN Bo, YUAN Yijun, et al. Radar HRRP recognition using attentional CNN with multi-resolution spectrograms[C]. 2019 International Radar Conference (RADAR), Toulon, France, 2019: 1–4.[3] WANG Penghui, DU Lan, PAN Mian, et al. Radar HRRP target recognition based on linear dynamic model[C]. 2011 IEEE CIE International Conference on Radar, Chengdu, China, 2011: 662–665.[4] YANG Xiuzhu, ZHANG Xinyue, DING Yi, et al. Indoor activity and vital sign monitoring for moving people with multiple radar data fusion[J]. Remote Sensing, 2021, 13(18): 3791. doi: 10.3390/rs13183791[5] YANG Jiachen, ZHANG Zhou, MAO Wei, et al. IoT-based critical infrastructure enabled radar information fusion[J]. Computers & Electrical Engineering, 2022, 98: 107723. doi: 10.1016/j.compeleceng.2022.107723[6] WU Hao, DAI Dahai, JI Penghui, et al. High-resolution range profile recognition method of vehicle targets based on accelerated T-SNE with multi-polarization fusion[C]. 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP), Nanjing, China, 2021: 72–76.[7] LIU Chang, ANTYPENKO R, SUSHKO I, et al. Marine distributed radar signal identification and classification based on deep learning[J]. Traitement du Signal, 2021, 38(5): 1541–1548. doi: 10.18280/ts.380531[8] HUANG Yuchen, LI Wei, DOU Zhiyang, et al. Activity recognition based on millimeter-wave radar by fusing point cloud and range-Doppler information[J]. Signals, 2022, 3(2): 266–283. doi: 10.3390/signals3020017[9] AGUILETA A A, BRENA R F, MAYORA O, et al. Multi-sensor fusion for activity recognition—A survey[J]. Sensors, 2019, 19(17): 3808. doi: 10.3390/s19173808[10] 周宁宁, 朱士涛, 年毅恒, 等. 一种基于多模态OAM波束的目标特征智能识别方法[J]. 雷达学报, 2021, 10(5): 760–772. doi: 10.12000/JR21056ZHOU Ningning, ZHU Shitao, NIAN Yiheng, et al. An intelligent target feature recognition method based on multi-mode OAM beams[J]. Journal of Radars, 2021, 10(5): 760–772. doi: 10.12000/JR21056[11] 邓冬虎, 张群, 罗迎, 等. 双基地ISAR系统中分辨率分析及微多普勒效应研究(英文)[J]. 雷达学报, 2013, 2(2): 152–167. doi: 10.3724/SP.J.1300.2013.13039DENG Donghu, ZHANG Qun, LUO Ying, et al. Resolution and micro-Doppler effect in Bi-ISAR system (in English)[J]. Journal of Radars, 2013, 2(2): 152–167. doi: 10.3724/SP.J.1300.2013.13039[12] 冯存前, 李靖卿, 贺思三, 等. 组网雷达中弹道目标微动特征提取与识别综述[J]. 雷达学报, 2015, 4(6): 609–620. doi: 10.12000/JR15084FENG Cunqian, LI Jingqing, HE Sisan, et al. Micro-Doppler feature extraction and recognition based on netted radar for ballistic targets[J]. Journal of Radars, 2015, 4(6): 609–620. doi: 10.12000/JR15084[13] 章鹏飞, 李刚, 霍超颖, 等. 基于双雷达微动特征融合的无人机分类识别[J]. 雷达学报, 2018, 7(5): 557–564. doi: 10.12000/JR18061ZHANG Pengfei, LI Gang, HUO Chaoying, et al. Classification of drones based on micro-Doppler radar signatures using dual radar sensors[J]. Journal of Radars, 2018, 7(5): 557–564. doi: 10.12000/JR18061[14] RYKUNOV M, DE GREEF E, KHALID H U R, et al. Multi-radar fusion for failure-tolerant vulnerable road users classification[C]. 2021 18th European Radar Conference (EuRAD), London, United Kingdom, 2022: 337–340.[15] SHU Haining and LIANG Qilian. Data fusion in a multi-target radar sensor network[C]. 2007 IEEE Radio and Wireless Symposium, Long Beach, USA, 2007: 129–132.[16] DU Lan, WANG Penghui, LIU Hongwei, et al. Bayesian spatiotemporal multitask learning for radar HRRP target recognition[J]. IEEE Transactions on Signal Processing, 2011, 59(7): 3182–3196. doi: 10.1109/TSP.2011.2141664[17] DEVLIN J, CHANG M W, LEE K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, USA, 2019: 4171–4186.[18] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: Transformers for image recognition at scale[C]. 9th International Conference on Learning Representations, Austria, 2021.[19] WANG Qiang, LI Bei, XIAO Tong, et al. Learning deep transformer models for machine translation[C]. 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 2019: 1810–1822.[20] CAO Kaidi, RONG Yu, LI Cheng, et al. Pose-robust face recognition via deep residual equivariant mapping[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 5187–5196.[21] SUN Yuanshuang, WANG Yinghua, LIU Hongwei, et al. SAR target recognition with limited training data based on angular rotation generative network[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(11): 1928–1932. doi: 10.1109/LGRS.2019.2958379[22] LOSHCHILOV I and HUTTER F. Decoupled weight decay regularization[C]. 7th International Conference on Learning Representations, New Orleans, USA, 2019.[23] YUAN Lele. A time-frequency feature fusion algorithm based on neural network for HRRP[J]. Progress in Electromagnetics Research M, 2017, 55: 63–71. doi: 10.2528/PIERM16123002[24] QUAN Daying, TANG Zeyu, WANG Xiaofeng, et al. LPI radar signal recognition based on dual-Channel CNN and feature fusion[J]. Symmetry, 2022, 14(3): 570. doi: 10.3390/sym14030570[25] ZHU Lijun. Selection of multi-level deep features via spearman rank correlation for synthetic aperture radar target recognition using decision fusion[J]. IEEE Access, 2020, 8: 133914–133927. doi: 10.1109/ACCESS.2020.3010969[26] CHEN Wenchao, CHEN Bo, PENG Xiaojun, et al. Tensor RNN with Bayesian nonparametric mixture for radar HRRP modeling and target recognition[J]. IEEE Transactions on Signal Processing, 2021, 69: 1995–2009. doi: 10.1109/TSP.2021.3065847[27] GUO Dandan, CHEN Bo, CHEN Wenchao, et al. Variational temporal deep generative model for radar HRRP target recognition[J]. IEEE Transactions on Signal Processing, 2020, 68: 5795–5809. doi: 10.1109/TSP.2020.3027470 -
Proportional views
- Publishing Ethics
- Journal Insights
- Abstracting & Indexing
- Peer Review Policies
- Guide for Authors
- ISSN 2095-283X (Print)ISSN 2097-339X (Online)
- CN 10-1030/TN
- CODEN LXEUAO
About Journal
- Sponsor: China Radio Detection and Ranging Industry Association (CRIA)
- Phone: 010-58887062
- Email：radars@aircas.ac.cn
- Publisher: Leida Xuebao Bianjibu (Editorial office of the Journal of Radars)
Contacts Us
京ICP备20021838号-8
Supported by: Beijing Renhe Information Technology Co. Ltd
Export File
Citation
Format
Content
DownLoad:
- Figure 1. Schematic of different fusion network structures
- Figure 2. Angle guided Transformer fusion network framework
- Figure 3. Patch embedding and positional embedding
- Figure 4. The Transformer module
- Figure 5. The angle guided module
- Figure 6. The pre-feature interaction module
- Figure 7. The deep attention feature fusion module
- Figure 8. Comparison of permutation invariant Transformer in feature fusion and Transformer in feature extraction
- Figure 9. Target HRRP examples for simulating multistation scenarios
- Figure 10. Confusion matrix of the recognition accuracy in test set (%)
- Figure 11. Visualization of test data and feature via two-dimensional t-SNE
- Figure 12. Accuracy and calculation amount changing with the number of HRRP subsequences