基于海洋浮标的电场干扰特性分析及信号检测方法

doi:10.12382/bgxb.2021.0671

摘要/Abstract

摘要：

为解决深远海电场信号获取和目标探测问题，对深海电场探测剖面浮标实测数据进行干扰电场特性分析，分析结果表明该浮标在深海剖面电场测量中，晃动干扰电场能量主要集中在0.5 Hz以下，且随着深度的增加而减小，干扰电场可低至0.1μV/m的量级。在干扰特性分析的基础上，为解决浮标平台的电场信号检测问题，提出基于自适应门限线谱能量加和的改进检测方法。仿真及实测实验结果表明：改进算法可有效检测目标电场信号，并在保证目标检测概率的情况下，减小了虚警概率。

关键词: 电场, 海洋浮标, 干扰特性, 目标检测

Abstract:

To solve the problems of electric field acquisition and target detection in the deep ocean environment, the interference characteristics of an electric field measured by a deep sea electric field detection buoy are analyzed. It has been confirmed that the energy of buoy shaking interference is primarily below 0.5 Hz and decreases with depth increases. The interference of the electric field can be reduced to 0.1μV/m. Then, to detect the electric field signals of the buoy platform, an improved detection method based on adaptive threshold line spectrum energy sum is proposed, based on the analysis of interference characteristics. Simulation and experimental results show that the proposed improved algorithm can effectively detect the target electric field signals and reduce the false alarm probability while ensuring target detection probability.

Key words: electric field, ocean buoy, interference characteristics, target detection

孙强, 张伽伟, 喻鹏. 基于海洋浮标的电场干扰特性分析及信号检测方法[J]. 兵工学报, 2023, 44(3): 857-864.

SUN Qiang, ZHANG Jiawei, YU Peng. Analysis of Electric Field Interference Characteristics and Signal Detection Method Based on Ocean Buoys[J]. Acta Armamentarii, 2023, 44(3): 857-864.

图/表 14

图1 水下电场探测浮标示意图

Fig. 1 Buoy for underwater electric field detection

图2 下潜过程中电场三分量及频谱

Fig. 2 Three components and spectrum of the electric field in the diving process

图3 下潜过程中浮标姿态变化

Fig. 3 Buoy attitude in the diving process

图4 悬浮过程中电场三分量及频谱

Fig. 4 Three components and spectrum of the electric field during floating

图5 悬浮过程中浮标姿态变化

Fig. 5 Buoy attitude during floating

图6 由悬浮到下潜过程中的电场

Fig. 6 Electric field from floating to diving

图7 由悬浮到下潜过程中浮标姿态变化

Fig.7 Buoy attitude from suspension to dive

图8 线谱能量检测流程

Fig. 8 Line spectrum energy detection process

图9 自适应门限的线谱能量加和检测流程

Fig. 9 Self-adaptive threshold line spectrum energy detection process

图10 仿真数据ROC曲线对比

Fig. 10 Comparison of ROC curves of simulation data

图11 实测数据ROC曲线对比

Fig. 11 Comparison of ROC curves of measured data

图12 两艘船舶实测低频电场

Fig. 12 Measured low-frequency electric fields of two real ships

图13 货船电场检测结果

Fig. 13 Electric field detection results of a cargo ship

图14 运油船电场检测结果

Fig. 14 Electric field detection results of a tanker

参考文献 20

[1]	武尚慧, 路静. 国外无人潜航器的发展现状与展望[J]. 电光系统, 2016, 156 (2): 6-12.
	WU S H, LU J. The current situation of developing foreign unmanned submarine navigation vehicle and looking into the distance[J]. Electronic and Electro-optical Systems, 2016, 156(2): 6-12. (in Chinese)
[2]	ROHAIZAD H R, MOHD Y M Y. Development of floating buoy technology using a modular method[J]. Design in Maritime Engineering, 2022, 167: 441-451.
[3]	刘增宏, 吴晓芬, 许建平, 等. 中国Argo海洋观测十五年[J]. 地球科学进展, 2016, 31(5): 445-460. doi: 10.11867/j.issn.1001-8166.2016.05.0445.
	LIU Z H, WU X F, XU J P, et al. Fifteen years of ocean observation with China argo[J]. Advances in Earth Science, 2016, 31(5):445-460. (in Chinese)
[4]	赵文春, 姜润翔, 喻鹏, 等. 基于轴频电场线谱特征的目标检测及识别[J]. 兵工学报, 2020, 41(6): 1165-1171. doi: 10.3969/j.issn.1000-1093.2020.06.013
	ZHAO W C, JIANG R X, YU P, et al. Detection and identification of ship shafe-rate electric field based on line-spectrum characteristics[J]. Acta Armamentarii, 2020, 41(6): 1165-1171. (in Chinese)
[5]	喻鹏, 程锦房, 张伽伟, 等. 基于Rao检测器的舰船轴频电场滑动门限检测方法[J]. 兵工学报, 2021, 42(4): 827-834. doi: 10.3969/j.issn.1000-1093.2021.04.016
	YU P, CHENG J F, ZHANG J W, et al. Ship shaft-rate electric field sliding threshold detection method based on Rao detector[J]. Acta Armamentarii, 2021, 42(4): 827-834. (in Chinese) doi: 10.3969/j.issn.1000-1093.2021.04.016
[6]	喻鹏, 程锦房, 张伽伟, 等. 基于自适应滤波的晃动相关电场噪声抑制方法[J]. 华中科技大学学报, 2021, 49(4): 61-66.
	YU P, CHENG J F, ZHANG J W, et al. Sway related electric field noise suppressing method based on adaptive filter[J]. Journal of Huazhong University of Science & Technology(Natural Science Edition), 2021, 49(4): 61-66. (in Chinese)
[7]	姜楷娜. 水下电场信号源小波时频分析[J]. 通信技术, 2020, 53(10): 2381-2388.
	JIANG K N. Wavelet time-frequency analysis of underwater electric field signal source[J]. Communication Technology, 2020, 53(10): 2381-2388. (in Chinese)
[8]	庞健, 秦明伟, 王焕, 等. 基于自适应门限的改进BigBand算法[J]. 宇航计测技术, 2021, 41(1): 53-57.
	PANG J, QIN M W, WANG H, et al. Improved BigBand algorithm based on adaptive threshold[J]. Journal of Astronautic Metrology and Measurement, 2021, 41(1): 53-57. (in Chinese)
[9]	李松, 石敏, 栾经德, 等. 舰船轴频电场信号特征提取与检测方法[J]. 兵工学报, 2015, 36(2): 220-224.
	LI S, SHI M, LUAN J D, et al. The feature extraction and detection for shafe-rate electric field of a ship[J]. Acta Armamentarii, 2015, 36(2): 220-224. (in Chinese)
[10]	程锐, 陈聪, 张伽伟. 基于EEMD 和改进功率谱熵的船舶轴频电场检测[J]. 华中科技大学学报(自然科学版), 2011, 39(11): 15-18.
	CHENG R, CHEN C, ZHANG J W. Detection of ship shafe-rate electric field based on EEMD and modified power spectral entropy[J]. Journal of Huazhong University of Science & Technology(Natural Science Edition), 2011, 39(11): 15-18. (in Chinese)
[11]	BAO Z H, HU P, GONG S G, et al. Detection of harmonics submerged in heavy and coloured noise based on wavelet packet decomposition[C]//Proceedings of 2010 International Conference on Computational and Information Sciences. Chengdu, China:IEEE, 2010:208-210.
[12]	胡鹏, 龚沈光, 胡英娣. 基于小波包熵的船舶轴频电场信号检测[J]. 华中科技大学学报(自然科学版), 2011, 39(11): 15-18.
	HU P, GONG S G, HU Y D. Detection of ships shaft-rate electric field signals using wavelet packet entropy[J]. Journal of Huazhong University of Science & Technology(Natural Science Edition), 2011, 39(11):15-18. (in Chinese)
[13]	DONATI R, LE C J P. Detection of oceanic electric fields based on the generalized likelihood ratio test (GLRT)[J]. IEEE Proceedings-Radar, Sonar and Navigation, 2002, 149(5): 221-230. doi: 10.1049/ip-rsn:20020491 URL
[14]	陈新刚, 喻鹏, 刘大钢. 基于自持式剖面浮标的目标电场探测方法研究[J]. 中国造船, 2020, 61 (A1): 31-39.
	CHEN X G, YU P, LIU D G. Underwater electric field detection method based on autonomous profiling drifter[J]. Ship Building of China, 2020, A1(61):31-39. (in Chinese)
[15]	林春生, 龚沈光. 舰船物理场[M]. 北京: 兵器工业出版社, 2007: 233-246.
	LIN C S, GONG S G. Ships physical fields[M]. Beijing Publishing House of Ordnance Industry, 2007: 233-246. (in Chinese)
[16]	SCHAEFER D, THIEL C, DOOSE J. Above water electric potential signatures of submerged naval vessels[J]. Journal of Marine Science and Engineering, 2019, 7(2): 1-12. doi: 10.3390/jmse7010001 URL
[17]	WANG J H, LI B, CHEN L P, et al. A novel detection method for underwater moving targets by measuring their ELF emissions with inductive sensors[J]. Sensors, 2017, 17(8): 17-34. doi: 10.3390/s17010017 URL
[18]	GARCIA ANTONIO S, SOLANO ADOLFO H, SAURA F J, et al. Underwater multi-influence measurements as a mean to characterize the overall vessel signature and protect the marine environment[J]. Ship Science and Technology, 2014, 7(14): 67-75.
[19]	YU P, ZHANG J W, CHNG J F, et al. Analysis of the natural electric field at different sea depths[J]. Journal of Instrumentation, 2021, 16, P01006:1-14.
[20]	CHENG D H, XU H G, GONG R L, et al. Ships matching based on an adaptive acoustic spectrum signature detection algorithm[J]. Signal & Image Processing: an International Journal (SIPIJ), 2018, 9(4):13-29.