{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T13:58:35Z","timestamp":1783605515917,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T00:00:00Z","timestamp":1706572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology on Electronic Information Control Laboratory","award":["JS20230100020"],"award-info":[{"award-number":["JS20230100020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, radar emitter signal recognition has enjoyed a wide range of applications in electronic support measure systems and communication security. More and more deep learning algorithms have been used to improve the recognition accuracy of radar emitter signals. However, complex deep learning algorithms and data preprocessing operations have a huge demand for computing power, which cannot meet the requirements of low power consumption and high real-time processing scenarios. Therefore, many research works have remained in the experimental stage and cannot be actually implemented. To tackle this problem, this paper proposes a resource reuse computing acceleration platform based on field programmable gate arrays (FPGA), and implements a one-dimensional (1D) convolutional neural network (CNN) and long short-term memory (LSTM) neural network (NN) model for radar emitter signal recognition, directly targeting the intermediate frequency (IF) data of radar emitter signal for classification and recognition. The implementation of the 1D-CNN-LSTM neural network on FPGA is realized by multiplexing the same systolic array to accomplish the parallel acceleration of 1D convolution and matrix vector multiplication operations. We implemented our network on Xilinx XCKU040 to evaluate the effectiveness of our proposed solution. Our experiments show that the system can achieve 7.34 giga operations per second (GOPS) data throughput with only 5.022 W power consumption when the radar emitter signal recognition rate is 96.53%, which greatly improves the energy efficiency ratio and real-time performance of the radar emitter recognition system.<\/jats:p>","DOI":"10.3390\/s24030889","type":"journal-article","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T12:06:58Z","timestamp":1706616418000},"page":"889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Efficient FPGA Implementation of Convolutional Neural Networks and Long Short-Term Memory for Radar Emitter Signal Recognition"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2755-6706","authenticated-orcid":false,"given":"Bin","family":"Wu","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5876-0698","authenticated-orcid":false,"given":"Xinyu","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youbing","family":"Gao","sequence":"additional","affiliation":[{"name":"Science and Technology on Electronic Information Control Laboratory, Chengdu 610036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangbo","family":"Si","sequence":"additional","affiliation":[{"name":"Integrated Service Networks Laboratory, Xidian University, Xi\u2019an 710071, China"},{"name":"Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naofal","family":"Al-Dhahir","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,30]]},"reference":[{"key":"ref_1","unstructured":"Tian, X., Liu, Y., Pan, X., and Chen, W. (2018, January 14\u201316). Intra-pulse Intentional Modulation Recognition of Radar Signals at Low SNR. Proceedings of the 2018 IEEE 2nd International Conference on Circuits, System and Simulation (ICCSS), Guangzhou, China."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, H., Jing, W., and Bai, Y. (2016, January 10\u201313). Radar emitter recognition based on deep learning architecture. Proceedings of the 2016 CIE International Conference on Radar (RADAR), Guangzhou, China.","DOI":"10.1109\/RADAR.2016.8059512"},{"key":"ref_3","first-page":"5341940","article-title":"Radar Emitter Individual Identification Based on Convolutional Neural Network Learning","volume":"2021","author":"Sun","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"54425","DOI":"10.1109\/ACCESS.2019.2913759","article-title":"Specific Emitter Identification Based on Deep Residual Networks","volume":"7","author":"Pan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Huang, J., Li, X., Wu, B., Wu, X., and Li, P. (2022). Few-Shot Radar Emitter Signal Recognition Based on Attention-Balanced Prototypical Network. Remote Sens., 14.","DOI":"10.3390\/rs14236101"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, Q., Han, L., Tan, R., Fan, H., Li, W., Zhu, H., Du, B., and Liu, S. (2021). Hybrid Attention Based Residual Network for Pansharpening. Remote Sens., 13.","DOI":"10.3390\/rs13101962"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, S., Pan, J., Han, Z., and Guo, L. (2021). Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrink-age Network. Sensors, 21.","DOI":"10.3390\/s21237973"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"012052","DOI":"10.1088\/1742-6596\/1682\/1\/012052","article-title":"Individual identification of communication radiation sources based on Inception and LSTM network","volume":"1682","author":"Chen","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wei, G., Hou, Y., Zhao, Z., Cui, Q., Deng, G., and Tao, X. (2018, January 12\u201314). Demo: FPGA-Cloud Architecture For CNN. Proceedings of the 2018 24th Asia-Pacific Conference on Communications (APCC), Ningbo, China.","DOI":"10.1109\/APCC.2018.8633447"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yan, T., Zhang, N., Li, J., Liu, W., and Chen, H. (2022). Automatic Deployment of Convolutional Neural Networks on FPGA for Spaceborne Remote Sensing Application. Remote Sens., 14.","DOI":"10.3390\/rs14133130"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, J., Huang, Y., Yang, H., Martinez, M., Hickman, G., Krolik, J., and Li, H. (2021, January 6\u20139). Efficient FPGA Implementation of a Convolutional Neural Network for Radar Signal Processing. Proceedings of the 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), Washington, DC, USA.","DOI":"10.1109\/AICAS51828.2021.9458573"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"He, D., He, J., Liu, J., Yang, J., Yan, Q., and Yang, Y. (2021). An FPGA-Based LSTM Acceleration Engine for Deep Learning Frameworks. Electronics, 10.","DOI":"10.3390\/electronics10060681"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2816","DOI":"10.1109\/TVLSI.2019.2941250","article-title":"High-Performance CNN Accelerator on FPGA Using Unified Winograd-GEMM Architecture","volume":"27","author":"Kala","year":"2019","journal-title":"IEEE Trans. Very Large Scale Integr. (VLSI) Syst."},{"key":"ref_14","first-page":"1415","article-title":"A CNN Accelerator on FPGA Using Depthwise Separable Convolution","volume":"65","author":"Bai","year":"2018","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"116569","DOI":"10.1109\/ACCESS.2020.3004198","article-title":"Sparse-YOLO: Hardware\/Software Co-Design of an FPGA Accelerator for YOLOv2","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yuan, S., Wu, B., and Li, P. (2021). Intra-Pulse Modulation Classification of Radar Emitter Signals Based on a 1-D Selective Kernel Convolutional Neural Network. Remote Sens., 13.","DOI":"10.3390\/rs13142799"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Wang, C., Zhou, R., Wang, X., Wang, H., and Yu, Y. (2022, January 15\u201317). A Specific Emitter Identification Method Based on RF-DNA and XGBoost. Proceedings of the 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi\u2019an, China.","DOI":"10.1109\/ICSP54964.2022.9778627"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, B., Yuan, S., Li, P., Jing, Z., Huang, S., and Zhao, Y. (2020). Radar Emitter Signal Recognition Based on One-Dimensional Convolutional Neural Network with Attention Mechanism. Sensors, 20.","DOI":"10.3390\/s20216350"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, X., Huang, G., Zhou, Z., and Gao, J. (2017, January 14\u201316). Radar emitter recognition based on the short time fourier transform and convolu-tional neural networks. Proceedings of the 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China.","DOI":"10.1109\/CISP-BMEI.2017.8302111"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sainath, T.N., Vinyals, O., Senior, A., and Sak, H. (2015, January 19\u201324). Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks. Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, QLD, Australia.","DOI":"10.1109\/ICASSP.2015.7178838"},{"key":"ref_22","unstructured":"Kolen, J.F., and Kremer, S.C. (2001). A Field Guide to Dynamical Recurrent Networks, IEEE."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zeng, S., Guo, K., Fang, S., Kang, J., Xie, D., Shan, Y., Wang, Y., and Yang, H. (2018, January 19\u201321). An Efficient Reconfigurable Framework for General Purpose CNN-RNN Models on FPGAs. Proceedings of the 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China.","DOI":"10.1109\/ICDSP.2018.8631880"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yuan, S., Li, P., and Wu, B. (2022). Towards Single-Component and Dual-Component Radar Emitter Signal Intra-Pulse Modulation Classification Based on Convolutional Neural Network and Transformer. Remote Sens., 14.","DOI":"10.3390\/rs14153690"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MC.1982.1653825","article-title":"Why systolic architectures?","volume":"15","author":"Kung","year":"1982","journal-title":"IEEE Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chang, K.-W., and Chang, T.-S. (2020, January 12\u201314). Efficient Accelerator for Dilated and Transposed Convolution with Decomposition. Proceedings of the 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Seville, Spain.","DOI":"10.1109\/ISCAS45731.2020.9180402"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/TCSI.2021.3131581","article-title":"A Flexible and Efficient FPGA Accelerator for Various Large-Scale and Lightweight CNNs","volume":"69","author":"Wu","year":"2022","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wu, S., Xu, Z., Wang, F., Yang, D., and Guo, G. (2021). An Improved Back-Projection Algorithm for GNSS-R BSAR Imaging Based on CPU and GPU Platform. Remote Sens., 13.","DOI":"10.3390\/rs13112107"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, S., Cao, J., Zhang, Q., Zhang, Q., Zhang, Y., and Wang, Y. (2020, January 8\u201312). An FPGA-Based Reconfigurable CNN Accelerator for YOLO. Proceedings of the 2020 IEEE 3rd International Conference on Electronics Technology (ICET), Chengdu, China.","DOI":"10.1109\/ICET49382.2020.9119500"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Cao, J., Zhang, Y., Zhang, S., Zhang, Q., and Yu, D. (2019, January 16\u201319). FPGA Implementation of Quantized Convolutional Neural Networks. Proceedings of the 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi\u2019an, China.","DOI":"10.1109\/ICCT46805.2019.8947168"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/889\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:51:30Z","timestamp":1760104290000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,30]]},"references-count":30,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24030889"],"URL":"https:\/\/doi.org\/10.3390\/s24030889","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,30]]}}}