Processing math: 100%
高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于空间相关电离层模型的天波雷达目标跟踪

郭振 王增福 兰华 潘泉

郭振, 王增福, 兰华, 潘泉. 基于空间相关电离层模型的天波雷达目标跟踪[J]. 电子与信息学报, 2022, 44(1): 354-362. doi: 10.11999/JEIT201030
引用本文: 郭振, 王增福, 兰华, 潘泉. 基于空间相关电离层模型的天波雷达目标跟踪[J]. 电子与信息学报, 2022, 44(1): 354-362. doi: 10.11999/JEIT201030
Zhou Bin, Hu Hong-Lin. Cooperative Device-to-Device (D2D) Forwarding Algorithms for Improving Data Dissemination Efficiency in Cellular Networks[J]. Journal of Electronics & Information Technology, 2012, 34(3): 704-709. doi: 10.3724/SP.J.1146.2011.00828
Citation: GUO Zhen, WANG Zengfu, LAN Hua, PAN Quan. Over-The-Horizon Radar Target Tracking Based on Spatial Correlation Ionosphere Model[J]. Journal of Electronics & Information Technology, 2022, 44(1): 354-362. doi: 10.11999/JEIT201030

基于空间相关电离层模型的天波雷达目标跟踪

doi: 10.11999/JEIT201030
基金项目: 国家自然科学基金 (61873211, 61790552),陕西省自然科学基础研究计划 (2021JM-067)
详细信息
    作者简介:

    郭振:男,1992年生,博士生,研究方向为信息融合与目标跟踪

    王增福:男,1982年生,副教授,研究方向为信息融合与目标跟踪、机器学习、路径规划、传感器管理等

    兰华:男,1986年生,副教授,研究方向为信息融合与目标跟踪、统计机器学习等

    潘泉:男,1961年生,教授,研究方向为信息融合与目标跟踪、模式识别与智能系统、信息安全与保密管理等

    通讯作者:

    王增福 wangzengfu@nwpu.edu.cn

  • 中图分类号: TN958.93

Over-The-Horizon Radar Target Tracking Based on Spatial Correlation Ionosphere Model

Funds: The National Natural Science Foundation of China (61873211, 61790552), The Natural Science Basic Research Plan in Shaanxi Province of China (2021JM-06)
  • 摘要: 天波超视距雷达(简称天波雷达)(OTHR)通过电离层反射效应可实现对多种高价值目标的远程预警。天波雷达目标跟踪算法设计中,电离层建模对其跟踪性能至关重要。该文考虑现实中电离层的空间相关性,提出一种基于高斯马尔可夫随机场(GMRF)的电离层虚高模型,以及相应的天波雷达多路径目标跟踪方法。该方法在贝叶斯估计的基础上,对多路径杂波环境下目标状态估计与电离层虚高参数进行联合建模与估计。该方法有效建立起了不同电离层区域之间的相关性,能够在电离层量测有限的情况下推断未量测区域的电离层虚高,改善电离层虚高参数辨识精度,进而提高目标跟踪精度。仿真结果表明基于空间相关性的电离层模型可以有效改善天波雷达目标跟踪性能。
  • 图  1  仿真场景示意图

    图  2  目标使用的电离层虚高的推断结果

    图  3  目标状态估计中的RMSE

    表  1  式(2)中传播模式与电离层参数的对应

    索引模式θtθr
    m=1EEhE(it)hE(ir)
    m=2EFhE(it)hF(ir)
    m=3FEhF(it)hE(ir)
    m=4FFhF(it)hF(ir)
    下载: 导出CSV

    表  2  量测预测及其协方差

     输入:hβ,HD×N,ˆxkk1
     输出:ˆymkk1,Smk
     (1) 根据预测的目标状态估计ˆxkk1计算电离层子区域索引itir,
     (2) 根据式(12)推断ˆhα|βˆΣα|β,
     (3) for m=1,2,3,4
     (4) 根据索引it,ir、模式m表1,在向量[(ˆhα)ThTβ]T中寻找
       电离层虚高向量ˆθk
     (5) 根据索引it,ir、模式m表1,在向量[(diag(ˆΣα|β))T
       (diag(ˆΣββ))T]T中寻找协方差矩阵Pθ的元素σ(θt)σ(θr)
     (6) 根据式(16)—式 (17),计算ˆymkk1Smk
     (7) end for
    下载: 导出CSV

    表  3  仿真场景参数设置

    参数 参数
    时间帧数总值 30 电离层区域(ΔX×ΔY) 15 km×15 km
    模式检测概率 0.7 每层子区域数目 144
    每帧杂波期望 50 E层电离层虚高标准差 10 km
    采样时间间隔(T) 20 s F层电离层虚高标准差 20 km
    监视区域规模(径向距) 1000 km~1400 km QE对角线元素 0.082
    监视区域规模(方位角) 4°~12° QE非对角线元素 –0.0205
    电离层区域(X) 480 km~750 km QF对角线元素 0.0587
    电离层区域(Y) 30 km~150 km QF非对角线元素 –0.0147
    下载: 导出CSV

    表  4  MPCR-GMRF, MPCR和MPDA的SSRSE对比

    算法径向距径向距速率方位角
    MPCR-GMRF6.06×10–40.0030060.004326
    MPCR8.58×10–40.0031150.004477
    MPDA12.26×10–40.0036890.005416
    下载: 导出CSV
  • [1] FABRIZIO G A. High Frequency Over-the-Horizon Radar: Fundamental Principles, Signal Processing, and Practical Applications[M]. New York: McGraw-Hill Education, 2013.
    [2] 周文瑜, 焦培南. 超视距雷达技术[M]. 北京: 电子工业出版社, 2008.

    ZHOU Wenyu and JIAO Peinan. Over-The-Horizon Radar[M]. Beijing: Publishing House of Electronics Industry, 2008.
    [3] PULFORD G W and EVANS R J. A multipath data association tracker for over-the-horizon radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(4): 1165–1183. doi: 10.1109/7.722704
    [4] LAN Hua, LIANG Yan, PAN Quan, et al. An EM algorithm for multipath state estimation in OTHR target tracking[J]. IEEE Transactions on Signal Processing, 2014, 62(11): 2814–2826. doi: 10.1109/TSP.2014.2318134
    [5] QIN Yong, MA Hong, CHEN Jinfeng, et al. Gaussian mixture probability hypothesis density filter for multipath multitarget tracking in over-the-horizon radar[J]. EURASIP Journal on Advances in Signal Processing, 2015, 2015: 108. doi: 10.1186/s13634-015-0294-y
    [6] TANG Xu, CHEN Xin, MCDONALD M, et al. A multiple-detection probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2015, 63(8): 2007–2019. doi: 10.1109/TSP.2015.2407322
    [7] HUANG Yuan, SHI Yifang, and SONG T L. An efficient multi-path multitarget tracking algorithm for over-the-horizon radar[J]. Sensors, 2019, 19(6): 1384. doi: 10.3390/s19061384
    [8] FENG Xiaoxue, LIANG Yan, ZHOU Lin, et al. Joint mode identification and localisation improvement of over-the-horizon radar with forward-based receivers[J]. IET Radar, Sonar & Navigation, 2014, 8(5): 490–500. doi: 10.1049/iet-rsn.2013.0017
    [9] LAN Hua, LIANG Yan, WANG Zengfu, et al. Distributed ECM algorithm for OTHR multipath target tracking with unknown ionospheric heights[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(1): 61–75. doi: 10.1109/JSTSP.2017.2787488
    [10] RUTTEN M G and PERCIVAL D J. Joint ionospheric and target state estimation for multipath OTHR track fusion[C]. Signal and Data Processing of Small Targets 2001, San Diego, USA, 2001: 118–129. doi: 10.1117/12.492797.
    [11] LAN Hua, WANG Zhengfu, BAI Xianglong, et al. Measurement-level target tracking fusion for over-the-horizon radar network using message passing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 57(3): 1600–1623. doi: 10.1109/TAES.2020.3044109
    [12] PULFORD G W. OTHR multipath tracking with uncertain coordinate registration[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(1): 38–56. doi: 10.1109/TAES.2004.1292141
    [13] GENG Hang, LIANG Yan, and CHENG Yuhua. Target state and markovian jump ionospheric height bias estimation for OTHR tracking systems[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50(7): 2599–2611. doi: 10.1109/TSMC.2018.2822819
    [14] GENG Hang, LIANG Yan, YANG Feng, et al. Joint estimation of target state and ionospheric height bias in over-the-horizon radar target tracking[J]. IET Radar, Sonar & Navigation, 2016, 10(7): 1153–1167. doi: 10.1049/iet-rsn.2015.0318
    [15] SOICHER H. Spatial correlation of transionospheric signal time delays[J]. IEEE Transactions on Antennas and Propagation, 1978, 26(2): 311–314. doi: 10.1109/TAP.1978.1141826
    [16] BUST G S, COKER C, COCO D S, et al. IRI data ingestion and ionospheric tomography[J]. Advances in Space Research, 2001, 27(1): 157–165. doi: 10.1016/S0273-1177(00)00163-0
    [17] YUE Xinan, WAN W, LIU Libo, et al. Statistical analysis on spatial correlation of ionospheric day-to-day variability by using GPS and Incoherent Scatter Radar observations[J]. Annales Geophysicae, 2007, 25(8): 1815–1825. doi: 10.5194/angeo-25-1815-2007
    [18] MINKWITZ D, VAN DEN BOOGAART K G, GERZEN T, et al. Tomography of the ionospheric electron density with geostatistical inversion[J]. Annales Geophysicae, 2015, 33(8): 1071–1079. doi: 10.5194/angeo-33-1071-2015
    [19] NORBERG J, VIERINEN J, ROININEN L, et al. Gaussian Markov random field priors in ionospheric 3-D multi-instrument tomography[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(12): 7009–7021. doi: 10.1109/TGRS.2018.2847026
    [20] NORBERG J, ROININEN L, VIERINEN J, et al. Ionospheric tomography in Bayesian framework with Gaussian Markov random field priors[J]. Radio Science, 2015, 50(2): 138–152. doi: 10.1002/2014RS005431
    [21] BOOKER H G and SEATON S L. Relation between actual and virtual ionospheric height[J]. Physical Review, 1940, 57(2): 87–94. doi: 10.1103/PhysRev.57.87
    [22] WHEADON N S, WHITEHOUSE J C, MILSOM J D, et al. Ionospheric modelling and target coordinate registration for HF sky-wave radars[C]. 1994 Sixth International Conference on HF Radio Systems and Techniques, York, UK, 1994: 258-266. doi: 10.1049/cp:19940504.
    [23] LI S Z. Markov Random Field Modeling in Image Analysis[M]. London: Springer Science & Business Media, 2009.
    [24] RUE H and HELD L. Gaussian Markov Random Fields. Theory and Applications[M]. Boca Raton: Chapman & Hall/CRC, 2005.
  • 加载中
图(3) / 表(4)
计量
  • 文章访问数:  1583
  • HTML全文浏览量:  533
  • PDF下载量:  146
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-12-07
  • 修回日期:  2021-05-27
  • 网络出版日期:  2021-08-16
  • 刊出日期:  2022-01-10

目录

    /

    返回文章
    返回