基于Rao检测器的舰船轴频电场滑动门限检测方法

doi:10.3969/j.issn.1000-1093.2021.04.016

摘要/Abstract

摘要： 为实现非高斯背景噪声和低信噪比情况下的舰船轴频电场检测，提出一种基于Rao检测器的滑动门限检测方法。基于信号源特征建立信号模型，在对水面浮动平台测量背景下环境噪声非高斯特性分析基础上，采用混合高斯模型对噪声进行建模；在检测过程中，实时估计混合高斯噪声模型参数和Rao检测统计量，并将前一段时间的平均Rao检测统计量作为门限，实现滑动门限检测。为验证所提方法的有效性，采用仿真计算的方式，证明Rao检测方法相比于能量检测方法的优势；根据实测数据，对比滑动门限Rao检测方法与滑动功率谱检测方法的性能差异。结果表明，滑动门限Rao检测方法能够有效抑制环境非高斯噪声影响，相比滑动功率谱检测方法具有更好的检测效果。

关键词: 舰船, 轴频电场, Rao检测器, 非高斯噪声, 滑动门限

Abstract: A sliding threshold detection method based on Rao detector is proposed to detect the ship's shaft-rate electric field at a low SNR in non-Gaussian noise environment. A signal model is established based on the characteristics of signal source, and a noise model is established based on Gaussian mixture model (GMM) after analyzing its non-Gaussian characteristics measured by a floating platform. In the detection process, the parameters of GMM and the Rao detection value are computed in real time; the mean value of previous Rao detection values is regarded as the sliding threshold. The simulation method is first used to verify the proposed method. The results show that the detection performance of Rao detector is better than that of energy detector. Then the measured ship data is used to compare the Rao sliding threshold method and the sliding power spectrum method. The results show that the proposed method is better in decreasing the non-Gaussian environment noise and has a better detection performance than the sliding power spectrum method.

Key words: ship, shaft-rateelectricfield, Raodetector, non-Gaussiannoise, slidingthreshold

中图分类号:

TJ610.2

喻鹏，程锦房，张伽伟，姜润翔. 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.

YU Peng， CHENG Jinfang， ZHANG Jiawei， JIANG Runxiang. Ship Shaft-rate Electric Field Sliding Threshold Detection Method Based on Rao Detector[J]. Acta Armamentarii, 2021, 42(4): 827-834.

参考文献

［1］林春生，龚沈光.舰船物理场［M］.第2版.北京：兵器工业出版社，2007.
LIN C S，GONG S G. Ship's physical fields［M］. 2nd ed. Beijing: Publishing House of Ordnance Industry,2007.(in Chinese)
［2］ MARIUS B. Variability of ship's electric signature during the RIMPASSE trial: DRDC-RDDC-2015-R272［R］. Canada: DRDC-Atlantic Research Center，2015.
［3］程锦房，张伽伟，姜润翔，等. 水下电磁探测技术的发展现状［J］. 数字海洋与水下攻防，2019，2（4）：45-49.
CHENG J F, ZHANG J W, JIANG R X,et al. Development status of underwater electromagnetic detection technology［J］. Digital Ocean & Underwater Warfare, 2019，2（4）：45-49. (in Chinese)
［4］张伽伟,喻鹏,姜润翔,等.基于舰船电场的目标跟踪方法研究［J］.兵工学报,2020,41(3):559-566.
ZHANG J W, YU P, JIANG R X, et al. Electric field tracking based on static potential difference ［J］. Acta Armamentarii, 2020,41(3): 559-566. (in Chinese)
［5］喻鹏. 舰船静电场隐身技术研究［D］. 武汉：海军工程大学，2017.
YU P. Research of ship's stealthy technology［D］. Wuhan: Navy University of Engineering, 2017. (in Chinese)
［6］吴建华．水下船体阴极保护与腐蚀电磁场优化控制研究［D］．大连：大连理工大学，2012.
WU J H. The optimization of hull cathodic protection and control of corrosion related electromagnetic field under seawater［D］. Dalian: Dalian University of Technology,2012. (in Chinese)
［7］包中华，于仕财，龚沈光.基于小波包分解和滑动功率谱的舰船轴频电场信号检测［J］.海军航空工程学院学报，2012，27（3）： 257-262.
BAO Z H, YU S C，GONG S G. Detection of ship shaft-rate electric field signals based on wavelet packet decomposition and sliding PSD［J］. Journal of Naval Aeronautical and Astronautical University, 2012，27（3）：257-262. (in Chinese)
［8］姜润翔，龚沈光. 基于AR模型参数的船舶轴频电场实时检测方法［J］. 哈尔滨工程大学学报，2013，34（8）：952-956.
JIANG R X, GONG S G. Vessel's shaft-related electric field signal detection based on the AR model parameter ［J］. Journal of Harbin Engineering University, 2013，34（8）：952-956. (in Chinese)
［9］程锐，姜润翔，龚沈光.基于EMD和4阶累积量的船舶轴频电场线谱提取［J］.舰船科学技术，2016，38（1）：94-98.
CHENG R, JIANG R X, GONG S G. Extraction of line spectrum of the ship shaft-rate electric field based on EMD and fourth-order cumulant［J］. Ship Science and Technology, 2016，38（1）：94-98. (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），2017，45（5）： 11-16.
CHENG R, CHEN C, ZHANG J W. Detection of ship shaft rate electric field based on EEMD and modified power spectral entropy［J］. Journal of Huazhong University of Science & Technology (Natural Science Edition), 2017，45（5）：11-16. (in Chinese)
［11］ DONATI R, CADRE J P. Detection of oceanic electric fields based on the generalized likelihood ratio test (GLRT)［J］. IEE Proceedings-Radar, Sonar and Navigation, 2002, 149(5): 221-230.
［12］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(增刊2)：220-224.
LI S, SHI M, LUAN J D, et al. The feature extraction and detection for shaft-rate electric field of a ship［J］. Acta Armamentarii, 2015,36(S2)：220-224. (in Chinese)
［13］ RAO C R. Linear statistical inference and its applications［M］. New York，NY，US: John Wiley & Sons, 1973.
［14］ CAO K T, YANG Z. A novel cooperative spectrum sensing algorithm based on random matrix theory［C］∥Proceedings of International Conference on Wireless Communications Networking and Mobile Computing. Chengdu, China: IEEE, 2010:1-4.
［15］ ZHU X, CHAMPAGNE B, ZHU W P. Rao test based cooperative spectrum sensing for cognitive radios in non-Gaussian noise ［J］. Signal Processing, 2014,97(1):183-194.
［16］ KAY S M, SENGUPTA D. Detection in incompletely characterized colored non-Gaussian autoregressive process［J］. IEEE Transactions on Signal Processing, 1993,41(10):3066-3070.
［17］朱晓梅. 认知无线电系统中非高斯噪声背景下频谱感知算法研究［D］. 南京：南京邮电大学，2014.
ZHU X M. Research on spectrum sensing in non-Gaussion noise for cognitive radio system［D］. Nanjing: Nanjing University of Posts and Telecommunications, 2014. (in Chinese)
［18］包中华，龚沈光，马珂，等. 实测海洋环境电场数据正态性检验［J］. 武汉理工大学学报（交通科学与工程版），2010，34（4）： 776-779.
BAO Z H, GONG S G, MA K, et al. Test of normality of mea- sured electric field data［J］. Journal of Wuhan University of Technology (Transportation Science and Engineering), 2010，34（4）：776-779. (in Chinese)
［19］ KAY S M. Fundamentals of statistical signal processing, volume Ⅲ: practical algorithm development ［M］. New York，NY，US: Pearson Education, 2013.
［20］ KAY S M. Fundamentals of statistical signal processing, volume 1: estimation theory ［M］. Upper Saddle River, NJ, US: Prentice Hall, 1998.
［21］方学前,王永良,王首勇.高斯混合噪声中的弱信号的Rao检测方法［J］.信号处理, 2009, 25(5):751-754.
FANG X Q, WANG Y L, WANG S Y. The Rao detection of weak signal in Gaussian mixture noise［J］.Signal Processing,2009, 25(5):751-754. (in Chinese)
［22］王平波，蔡志明，刘旺锁. 混合高斯概率密度模型参数的期望最大化估计［J］. 声学技术，2007，26（3）：498-502.
WANG P B, CAI Z M, LIU W S. EM estimation of PDF parameters for Gaussian mixture processes［J］. Technical Acoustics, 2007，26（3）：498-502. (in Chinese)

[1]	王锴松，周国华，刘月林，王玉芬，刘胜道. 地磁模拟法测量舰船感应磁场的数值模拟[J]. 兵工学报, 2022, 43(3): 617-625.
[2]	郭成豹，胡松，王文井，殷琦琦. 利用磁传感器阵列磁场差值的舰船磁场反演建模方法[J]. 兵工学报, 2022, 43(1): 111-119.
[3]	徐英，谷雨，彭冬亮，刘俊，陈华杰. 面向合成孔径雷达图像任意方向舰船检测的改进YOLOv3模型[J]. 兵工学报, 2021, 42(8): 1698-1707.
[4]	王逸南，张建伟，王治，姚熊亮，杨娜娜. 基于板厚补偿的921A钢与Q345钢靶板在截卵形弹体侵彻下的等效方法[J]. 兵工学报, 2021, 42(11): 2465-2475.
[5]	王成，吴岩，杨廷飞. 利用改进单分类支持向量机提升舰船尾流目标的检测准确率[J]. 兵工学报, 2020, 41(9): 1887-1893.
[6]	赵文春，姜润翔, 喻鹏，张伽伟. 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.
[7]	陈浩，潘逊，肖大为. 模拟舰船磁场的两轴磁体设计研究[J]. 兵工学报, 2020, 41(6): 1172-1178.
[8]	孙强，姜润翔，喻鹏，程锦房. 轴频电场与静电场一体化腐蚀相关电场隐身方法研究[J]. 兵工学报, 2020, 41(4): 730-736.
[9]	李茂，侯海量，李典，陈鹏宇，李永清，朱锡. 动态爆炸战斗部对舰船舱壁的破片载荷特性研究[J]. 兵工学报, 2019, 40(9): 1804-1818.
[10]	杨龙，苏娟，李响. 基于生成式对抗网络的合成孔径雷达舰船数据增广在改进单次多盒检测器中的应用[J]. 兵工学报, 2019, 40(12): 2488-2496.
[11]	覃若琳，蒋晓刚，金良安，高可心. 基于Hough变换的水中气泡群特征参数提取方法研究[J]. 兵工学报, 2019, 40(12): 2504-2512.
[12]	孙嘉庆, 陈聪, 危玉倩, 李定国. 微分递推法在舰船静态电场深度换算中的应用研究[J]. 兵工学报, 2018, 39(2): 345-355.
[13]	张旭，崔乃刚，王小刚，崔祜涛，秦武韬. 一种鲁棒自适应容积卡尔曼滤波方法及其在相对导航中的应用[J]. 兵工学报, 2018, 39(1): 94-100.
[14]	姜润翔，张伽伟，林春生. 基于点电荷模型的腐蚀相关静电场快速预测方法研究[J]. 兵工学报, 2017, 38(4): 735-743.
[15]	孟路稳，程广利，陈亚男，张明敏. 舰船地震波传播机理及其在水雷引信中的应用研究[J]. 兵工学报, 2017, 38(2): 319-325.