| 199 | 0 | 10 |
| Downloads | Citas | Reads |
[Objective] There is growing demand for accurate ocean environmental monitoring and spectrum sensing technology, and traditional single-sensor systems are no longer enough to meet the technical needs of multidimensional parameter collection and analysis. To address this challenge, we developed a custom maritime monitoring system designed to achieve efficient, stable, and continuous observations of both oceanic meteorological conditions and electromagnetic spectrum distribution. [Methods] This intelligent platform with multimodal sensing capabilities integrates five main functional modules: a positioning and attitude sensing unit, a meteorological parameter collection unit, a data storage and management unit, a remote communication module, and an electromagnetic spectrum reception module. It allows for the synchronized collection and local processing of more than ten key parameters, including wind speed, wind direction, attitude angles, power supply data, and electromagnetic signal strength. Structurally, the platform features a modular, low-power design, making it suitable for long-distance deployment and mid-to long-term operation, greatly enhancing deployment flexibility and data continuity on the ocean surface. The principles and procedures of electromagnetic signal reception are described in detail here. Regarding the field measurement process, sensor attitude was examined, and a preliminary analysis was performed on how platform orientation and meteorological factors(such as wind speed and direction) influence signal strength. A compensation model for platform attitude was then proposed. To evaluate the platform's performance, it was deployed in a typical dynamic maritime environment to continuously record the received signal strength indicator(RSSI) within a target frequency band. Real-time attitude data(pitch, roll, and yaw) and meteorological variables(wind speed and direction) were recorded simultaneously. Using the collected data, both linear and nonlinear multivariate regression models were developed to analyze how platform motion and environmental disturbances impact signal reception. Linear models proved insufficient to capture the complexity of attitude and meteorological influences on signal strength, leading to the adoption of a random forest regression algorithm, which demonstrated strong performance in handling nonlinear and multivariable interactions. [Results] Results reveal a significant negative correlation between yaw angle and RSSI, with wind direction deviation also exerting a moderate influence on signal strength. When compared to the XGBoost and SVR algorithms, the random forest model showed superior accuracy and stability, achieving an R2 value greater than 0.84, indicating a high explanatory power of platform attitude and environmental factors on signal strength variations. [Conclusions] This study is the first to incorporate multi-source heterogeneous environmental sensing data into signal strength modeling. It introduces a novel analytical framework tailored for dynamic marine environments that improves spectrum sensing accuracy and stability on mobile platforms. Both theoretical insights and experimental validation are provided. The proposed system and methodology demonstrate strong scalability and broad applicability, suitable for uses such as marine meteorological monitoring, maritime electromagnetic interference detection, and wireless communication link assessment. Additionally, this system will serve as a valuable educational tool in universities, supporting both teaching and practical training.
[1]CHEN Z J, ZHANG Y H. Offshore electromagnetic spectrum distribution prediction algorithm based on ray tracing method[J]. IEEE Antennas and Wireless Propagation Letters, 2019,18(10):2061–2065.
[2]CHEN Z J, ZHANG Y H. Offshore electromagnetic spectrum distribution prediction model based on ray tracing method and pm wave spectrum[J]. IEEE Access, 2019, 7:174298–174311.
[3]崔翠梅,殷昌永,杨德智.未来复杂网络环境下动态频谱高效感知技术研究进展[J].电讯技术, 2025, 65(4):634–641.CUI C M, YIN C Y, YANG D Z. Dynamic spectrum efficient sensing under future complex network environments:State-ofthe-art and recent advances[J]. Telecommunication Engineering,2025, 65(4):634–641.(in Chinese)
[4]韩晨,张誌,段保平,等.基于蒙特卡洛方法的移动通信系统对卫星通信系统动态频谱干扰分析与设计[J].电信科学,2024(增刊2):30–37.HAN C, ZHANG Z, DUAN B P, et al. Analysis and design of dynamic spectrum interference in mobile communication systems to satellite communication systems based on Monte Carlo method[J]. Telecommunications Science, 2024(S2):30–37.(in Chinese)
[5]鲁华超,赵知劲,尚俊娜,等.利用卷积神经网络和协方差的协作频谱感知算法[J].信号处理, 2019, 35(10):1700–1707.LU H C, ZHAO Z J, SHANG J N, et al. Cooperative spectrum sensing algorithm using convolutional neural networks and covariance[J]. Journal of Signal Processing, 2019, 35(10):1700–1707.(in Chinese)
[6]CHEN Z J, ZHANG Y H. Offshore electromagnetic spectrum detection system based on self-organizing network[J]. IEEE Sensors Journal, 2021, 21(6):8650–8661.
[7]张超,熊春宝,连思达.基于深度学习的海洋平台基础环境监测方法[J].测绘通报, 2025(6):12–17.ZHANG C, XIONG C B, LIAN S D. Deep learning-based method for foundation environmental monitoring of offshore platform[J]. Bulletin of Surveying and Mapping, 2025(6):12–17.(in Chinese)
[8]陈义奇,钟恒,陈光耀.面向多传感器融合的物联网安全监测系统设计[J].智能物联技术, 2025, 57(5):42–45.CHEN Y Q, ZHONG H, CHEN G Y. Design of internet of things security monitoring system with multi-sensor fusion[J].Technology of IoT&AI, 2025, 57(5):42–45.(in Chinese)
[9]于子敏,常奋华,李睿,等.智能风速风向采集节点系统的测量与控制研究[J].计算机仿真, 2025, 42(8):325–331.YU Z M, CHANG F H, LI R, et al. Research on measurement and control of intelligent wind speed and direction acquisition node system[J]. Computer Simulation, 2025, 42(8):325–331.(in Chinese)
[10]郭炜辰,洪钰松,王增,等.基于快速傅里叶变换的某超高精密级恒温空调水系统运行波动与扰量特性研究[J].建筑科学, 2025, 41(6):182–189.GUO W C, HONG Y S, WANG Z, et al. Operational fluctuations and perturbation analysis of an ultra-precision constant-temperature air-conditioning water system based on fast Fourier transform[J]. Building Science, 2025, 41(6):182–189.(in Chinese)
[11]YUCEK T, ARSLAN H. A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys&Tutorials, 2009, 11(1):116–130.
[12]石义芳,李真,叶沛欣.基于分组约束的集群机器人协同定位实验平台设计[J].实验技术与管理, 2025, 42(5):54–62.SHI Y F, LI Z, YE P X. Design of an experimental platform for swarm robotics cooperative localization based on grouping constraints[J]. Experimental Technology and Management,2025, 42(5):54–62.(in Chinese)
[13]杨雪林,王西泉,张旭斌,等.基于空间三维欧拉角估计的靶场外弹道数据处理方法研究[J].兵器装备工程学报, 2024,45(4):75–81.YANG X L, WANG X Q, ZHANG X B, et al. Study on the data processing method of range external ballistics based on threedimensional Euler angle estimation[J]. Journal of Ordnance Equipment Engineering, 2024, 45(4):75–81.(in Chinese)
[14]刘龙波,乔记平,刘儒平,等.基于RSSI的RFID室内定位算法优化研究[J].计算机应用与软件, 2025, 42(2):152–156.LIU L B, QIAO J P, LIU R P, et al. Optimization of RFID indoor positioning algorithm based on RSSI[J]. Computer Applications and Software, 2025, 42(2):152–156.(in Chinese)
[15]KIM S, BAHMANI S, LEE K. Max-linear regression by convex programming[J]. IEEE Transactions on Information Theory,2024, 70(3):1897–1912
[16]周星宇,钱新博,王敦凯,等.基于经验模态分解和随机森林的电磁换向阀寿命预测[J].液压气动与密封, 2025, 45(5):1–8.ZHOU X Y, QIAN X B, WANG D K, et al. Life prediction of solenoid directional valve based on empirical modedecomposition and random forest[J]. Hydraulics Pneumatics&Seals, 2025,45(5):1–8.(in Chinese)
[17]SUN H, LIANG L Q, WANG C L, et al. Prediction of the electrical strength and boiling temperature of the substitutes for greenhouse gas SF6 using neural network and random forest[J].IEEE Access, 2020, 8:124204–124216.
[18]谢世元,郭志远,强惠敏,等.基于XGBoost算法的船舶目的地预测模型[J].计算机工程与设计, 2025, 46(6):1766–1771.XIE S Y, GUO Z Y, QIANG H M, et al. Vessel destination prediction model based on XGBoost algorithm[J]. Computer Engineering and Design, 2025, 46(6):1766–1771.(in Chinese)
[19]DAI D, MAO X, LIU Y H, et al. AdaBoost-SVR model-based transmitted microwave sensing in wheat moisture prediction[J].IEEE Sensors Journal, 2024, 24(3):3988–3998.
Basic Information:
DOI：10.16791/j.cnki.sjg.2026.03.002
China Classification Code:TN98
Citation Information:
[1]LIU Yanlong,CHEN Zhenjia.Electromagnetic spectrum sensing experiments and prediction models in the marine environment[J].Experimental Technology and Management,2026,43(03):10-17.DOI:10.16791/j.cnki.sjg.2026.03.002.
Fund Information:
海南大学学位与研究生教学改革研究项目(HDJG-Y202527);海南大学研究生创新能力提升建设项目-培养基地建设项目(HDPYJD2025Y0001); 海南省高等学校教育教学改革研究项目(Hnjg2025ZD-9)
2025-11-11
2025-11-11
2025-11-11