| 7 | 0 | 0 |
 Reads |
 Downloads |
 Citas |
Machine learning and microwave recognition technology can offer smarter solutions for bend warning systems. A low⁃cost 10.52 GHz microwave radar is used to detect moving targets on the road. By analyzing the radar echo characteristics of vehicles, two ⁃ wheelers, and pedestrians, 12 feature parameters are defined and extracted from the signal′s time ⁃ frequency diagram, frequency⁃amplitude plot, and instantaneous Doppler frequency⁃time curve, so as to construct feature vectors. A three⁃class dataset is created with pedestrians, two⁃wheelers, and vehicles as the target categories, and the SMOTE (synthetic minority oversampling technique) algorithm is applied to eliminate the class imbalance of the dataset. The XGBoost (extreme gradient boosting) algorithm model is examined, and after optimization using the Bayesian optimization algorithm (BOA), its macro⁃average accuracy rate for target recognition reaches 95.1%. Finally, an intelligent bend warning system is designed based on this microwave identification technology. To sum up, this scheme has a certain practical value.
[1] Tanaka T S T, Wang Sheng, Jørgensen J R, et al. Review of crop phenotyping in field plot experiments using UAV⁃mounted sensors and algorithms [J]. Drones, 2024, 8: 212.
[2] Asadzadeh S, De Oliveira W J, De Souza Filho C R. UAV⁃based remote sensing for the petroleum industry and environmental monitoring: State ⁃ of ⁃ the ⁃ art and perspectives [J]. Journal of Petroleum Science and Engineering, 2022, 208: 109633.
[3] Mohdnoor N, Abdullah A, Hashim M. Remote sensing UAV/ drones and its applications for urban areas [J]. IOP Conference Series: Earth and Environmental Science, 2018, 169(1): 12003.
[4] Erdelj M, Natalizio E. UAV ⁃ assisted disaster management: Applications and open issues [C]// Proceedings of the 2016 International Conference on Computing, Networking and Communications (ICNC). Kauai, HI, USA: IEEE, 2016: 1⁃5.
[5] Zolf K. Gold rush optimizer: a new population⁃based metaheuristic algorithm [J]. Operations Research and Decisions, 2023, 33(1): 113⁃150.
[6] 谢勇宏，孔月萍.基于改进粒子群算法的三维路径规划[J].计算机测量与控制，2022，30（3）：179⁃182.
[7] Abdel⁃Basset M, Mohamed R, Jameel M, et al. Spider wasp optimizer: a novel meta ⁃ heuristic optimization algorithm [J]. Artificial Intelligence Review, 2023, 56(10): 11675⁃11738.
[8] Mohamed A B, Reda M, Shaimaa A, et al. Kepler optimization algorithm: A new metaheuristic algorithm inspired by Kepler′s laws of planetary motion [J]. Knowledge⁃based Systems, 2023, 268: 110454.
[9] Abasi A K, Makhadmeh S N, Al⁃Betar M A, et al. Lemurs optimizer: A new metaheuristic algorithm for global optimization [J]. Applied Sciences, 2022, 12(19): 10057.
[10] Das S, Suganthan P N. Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems [R]. Kolkata, India: [s.n.], 2010: 341⁃359.
[11] Salgotra R, Singh U, Saha S, et al. Improving cuckoo search: incorporating changes for CEC 2017 and CEC 2020 benchmark problems [C]// 2020 IEEE Congress on Evolutionary Computation (CEC). Glasgow, United Kingdom: IEEE, 2020: 1⁃7.
[12] Jia Heming, Rao Honghua, Wen Changsheng, et al. Crayfish optimization algorithm [J]. Artificial Intelligence Review, 2023, 56(S2): 1919⁃1979.
[13] 程翠娜，奉松绿，莫礼平.基于正余弦优化算子和Lévy飞行机制的和声搜索算法[J].数据采集与处理，2023，38（3）：690⁃703.
[14] Menon A, Mehrotra K, Mohan C K, et al. Characterization of a class of sigmoid functions with applications to neural networks [J]. Neural Networks, 1996, 9(5): 819⁃835.
[15] 李真，王帆，王冉珺.一种结合灰狼算法的粒子群优化算法[J].计算机测量与控制，2021，29（10）：217⁃222.
[16] 朱泽锋，胡金磊，温佳静.变电站无人机巡检障碍物状态量化准确性评价[J].计算机仿真，2022，39（4）：387⁃391.
[17] 胡中华，许昕，陈中.无人机三维航迹非均匀三次B样条平滑算法[J].控制工程，2020，27（7）：1259⁃1266.
[18] 王小璐，黄辰，于远航，等.基于适应度值优劣粒子群算法的无人机路径规划[J].电子制作，2022，30（16）：16⁃19.
[19] 胡观凯，钟建华，李永正，等.基于IPSO⁃GA算法的无人机三维路径规划[J].现代电子技术，2023，46（7）：115⁃120.
[20] 袁建华，李尚.无人机三维路径规划及避障方法[J].信息与控制，2021，50（1）：95⁃101.
[21] Abdel ⁃ Basset M, Mohamed R, Sallam K M, et al. Light spectrum optimizer: a novel physics ⁃ inspired metaheuristic optimization algorithm [J]. Mathematics, 2022, 10(19): 3466.
Basic Information:
DOI：10.16652/j.issn.1004⁃373x.2026.09.027
Citation Information:
[1]Yan Chengfeng, Xiong Lun, Lu Yongxiong.BO⁃XGBoost⁃based microwave recognition and early warning technology for bend traffic[J].Modern Electronic Technique,2026(9):185-190.DOI:10.16652/j.issn.1004⁃373x.2026.09.027.