基于轮廓重建的渔船雷达MAP-MRF快速反演研究

郑建丽; 曹建军; 刘洋

doi:10.19998/j.cnki.2095-1795.2023.01.004

基于轮廓重建的渔船雷达MAP-MRF快速反演研究

doi: 10.19998/j.cnki.2095-1795.2023.01.004

1.
中国水产科学研究院渔业机械仪器研究所，上海 200092
2.
辽宁师范大学计算机与信息技术学院，辽宁大连 116081

基金项目: 崂山实验室科技创新项目（LSKJ202201805）；国家自然科学基金项目（32073026）；辽宁省“兴辽英才计划”资助项目（XLYC1807073）；大连市支持高层次人才创新创业项目（2019RQ037）

详细信息

作者简介:
郑建丽，硕士，副研究员，主要从事渔船技术法规研究，开展法规体系、政策及检验制度相关工作E-mail：zhengjianli@fmiri.ac.cn

刘洋，通信作者，博士，讲师，主要从事图像识别、深度学习、雷达图像应用和工业图像缺陷检测研究E-mail：yangliu.0816@lnnu.edu.cn

中图分类号: TP391.7
计量
- 文章访问数: 57
- HTML全文浏览量: 21
- PDF下载量: 16
- 被引次数: 0
出版历程
- 收稿日期: 2022-07-18
- 修回日期: 2022-10-04
- 出版日期: 2023-01-20

Rapid Retrieval of Shipborne Fishing Vessel Radar by MAP-MRF Based on Contour Reconstruction

1.
Fishery Machinery and Instrument Research Institute，Chinese Academy of Fishery Sciences，Shanghai 200092，China
2.
School of Computer and Information Technology，Liaoning Normal University，Dalian Liaoning 116081，China

摘要

摘要:
渔船雷达是渔船导航系统的重要组成部分，在海上复杂气象条件下，具有不受能见度影响进行全天候观测的优势。从雷达信号向可判读图像信号的反演过程中，对雷达照射夹角间的缺失数据进行重建是反演过程中计算和时间花费最大的部分。依据渔船反演过程中的特性，将统计力学模型引入到雷达图层轮廓的重建计算中，应用最大后验概率马尔可夫随机场框架实现了渔船雷达的快速雷达图像反演过程。研究成果对于减少反演时间，提高雷达刷新率，及时准确地发现海上强对流灾害目标具有重要的意义和实用价值。
- 渔船雷达 /
- 反演 /
- 马尔可夫随机场 /
- 轮廓 /
- 插值
Abstract:
Fishing vessel radar is an important part of shipborne system, which has advantage of observation without being affected by visibility under complicated meteorological conditions at sea．In inversion process from radar signal to interpretable image signal, reconstruction of missing data between angle of radar irradiation is the most time-consuming part in inversion process．Based on characteristics of meteorological radar inversion, statistical mechanics model was introduced into reconstruction calculation of radar layer contour, and Maximum a Posteriori-Markov Random Field（MAP-MRF）was applied．MAP-MRF framework realized fast radar image retrieval process of shipborne meteorological radar．The proposed method reduced inversion time and improve radar refresh rate so as to timely and accurately discover marine severe convection disaster targets．
- fishing vessel radar /
- retrieval /
- MRF /
- contour /
- interpolation

HTML全文

图 1 基于轮廓重建的快速反演流程

Figure 1. Flowchart of rapid retrieval based on contour reconstruction

下载: 全尺寸图片幻灯片

图 2 雷达基数据文件成像

Figure 2. Radar base data file imaging

下载: 全尺寸图片幻灯片

图 3 雷达基数据图像去噪比较

Figure 3. Comparison of radar cardinality according to image denoising

下载: 全尺寸图片幻灯片

图 4 雷达图层反演结果

Figure 4. Retrieval results of radar layers

下载: 全尺寸图片幻灯片

图 5 雷达图像反演结果

Figure 5. Radar image retrieval results

下载: 全尺寸图片幻灯片

图 6 文中方法与传统方法运行时间对比

Figure 6. Processing time of proposed method compared with traditional methods

下载: 全尺寸图片幻灯片

表 1 数据重建准确率对比

Table 1. Data reconstruction accuracy comparison

方法准确率

本文方法 0.878
最邻近插值法 0.827
双线性插值法 0.904
Barnes分析法 0.931

下载: 导出CSV

参考文献(32)

[1]	IEC63154．Maritime navigation and radio communication equipment and systems-cybersecurity: general requirements, methods of testing and required test results[S]．Switzerland: International Electrotechnical Commission, 2021．
[2]	MALAMUD B D,TURCOTTE D L.Statistics of severe tornadoes and severe tornado outbreaks[J].Atmospheric Chemistry and Physics,2012,12(8): 8459 - 8473.
[3]	吴雄斌,李伦,李炎,等.高频地波雷达海面有效波高探测实验研究[J].海洋与湖沼,2012,43(2):210-226. doi: 10.11693/hyhz201202002002 WU Xiongbin,LI Lun,LI Yan,et al.Experimental research on significant waveheight detecting with HFSWR OSMAR071[J].Oceanologia et Limnologia Sinica,2012,43(2):210-226. doi: 10.11693/hyhz201202002002
[4]	宫玉坤,柏宇,马意彭.浅析海杂波对雷达检测的影响[J].中国新通信,2018,20(11):239. doi: 10.3969/j.issn.1673-4866.2018.11.200
[5]	姚春雨,陈章友,吴雄斌,等.一种应用于X波段海洋监测雷达的插值算法研究[J].科学技术与工程,2015,15(34):64-68. doi: 10.3969/j.issn.1671-1815.2015.34.011 YAO Chunyu,CHEN Zhangyou,WU Xiongbin,et al.A study on interpolation algorithms applied to marine monitoring of X-band radar[J].Science Technology and Engineering,2015,15(34):64- 68. doi: 10.3969/j.issn.1671-1815.2015.34.011
[6]	SHI L,HESS K W,MYERS E P.Spatial interpolation of tidal data using a multiple-order harmonic equation for unstructured grids[J].International Journal of Geosciences,2013,4(10): 1425 - 1437. doi: 10.4236/ijg.2013.410140
[7]	FREDERICK H M,JAMES E C,GEORGE P C.An operational objective analysis system[J].Monthly Weather Review,1959,87(10):367-374. doi: 10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2
[8]	SHAPIRO G I,GONZALEZ-ONDINA J M,BELOKOPYTO V V N.High resolution stochastic downscaling method for ocean forecasting models and its application to the Red Sea dynamics[J].Ocean Science,2021,17(4):891-907. doi: 10.5194/os-17-891-2021
[9]	DEB S K,SANKHALA D,JAISWAL N.Wind derived products using INSAT-3D atmospheric motion vectors and its meteorological applications[J].International Journal of Remote Sensing,2021,42(4): 1357 - 1378. doi: 10.1080/01431161.2020.1829153
[10]	MIAO H,DONG D,HUANG G,et al.Evaluation of northern hemisphere surface wind speed and wind power density in multiple reanalysis datasets[J].Energy,2020(1): 117382 .
[11]	SUN Min, WANG Haijiang, LI Zhaoming , et al．Study on reflectivity data interpolation and mosaics for multiple Doppler weather radars [J]．EURASIP Journal on Wireless Communications and Networking, 2019．DOI: 10.1186/s13638-019-1465-6．
[12]	MERINO A,GARCIA-ORTEGA E,NAVARRO A,et al.Evaluation of gridded rain-gauge-based precipitation datasets:impact of station density,spatial resolution,altitude gradient and climate[J].International Journal of Climatology,2021,41(5): 3027 - 3043. doi: 10.1002/joc.7003
[13]	XU C, WEI T．A wavelet and PDE method for optical image interpolation[C]// International Conference on Information Science & Engineering．IEEE Press, 2011: 3 498-3 501．
[14]	RUZANSKI E,CHANDRASEKAR V.Weather radar data interpolation using a kernel-based lagrangian nowcasting technique[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(6): 3073 - 3083. doi: 10.1109/TGRS.2014.2368076
[15]	DONG W,ZHANG L,LUKAC R,et al.Sparse representation based image interpolation with nonlocal autoregressive modeling[J].IEEE Transactions on Image Processing,2013,22(4): 1382 - 1394. doi: 10.1109/TIP.2012.2231086
[16]	SHI W, CABALLERO J, F HUSZÁR, et al．Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition, IEEE Press, 2016．
[17]	JENSEN K,ANASTASSIOU D.Subpixel edge localization and the interpolation of still images[J].IEEE Transactions on Image Processing,1995,4(3):285-295. doi: 10.1109/83.366477
[18]	LEI Z,WU X.An edge-guided image interpolation algorithm via directional filtering and data fusion[J].IEEE Transactions on Image Processing,2006,15(8): 2226 - 2238. doi: 10.1109/TIP.2006.877407
[19]	许爱国,陈杰,宋家辉,等.多相流系统的离散玻尔兹曼研究进展[J].空气动力学学报,2021(3):138-169. doi: 10.7638/kqdlxxb-2021.0021 XU Aiguo,CHEN Jie,SONG Jiahui,et al.Progress of discrete Boltzmann study on multiphase complex flows[J].Acta Aerodynamica Sinica,2021(3):138-169. doi: 10.7638/kqdlxxb-2021.0021
[20]	李金凤,楚晓亮,王剑,等.X波段雷达图像反演风场光流方法的改进[J].中国海洋大学学报(自然科学版),2014(4):104- 107,118. LI Jinfeng,CHU Xiaoliang,WANG Jian,et al.Improving research on the optical flow method of retrieving ocean wind field from X-band radar images[J].Periodical of Ocean University of China,2014(4):104- 107,118.
[21]	MORGENSTERN I,BINDER K,HORNREICH R M.Two-dimensional Ising model in random magnetic fields[J].Physical Review B,1981,23(1):287-297. doi: 10.1103/PhysRevB.23.287
[22]	LEE T D,YANG C N.Statistical theory of equations of state and phase transitions II lattice gas and ising model[J].Physical Review,1952,87(3):410-419. doi: 10.1103/PhysRev.87.410
[23]	宋燕,李伟.降低雷达接收机中噪声的几种方法[J].科技创新导报,2019,16(7): 155,157. doi: 10.16660/j.cnki.1674-098X.2019.07.155
[24]	唐民,李旭.分析降低雷达接收机中噪声的几种方法[J].科学与信息化,2020(11):191.
[25]	BESAG J.Spatial interaction and the statistical analysis of lattice systems[J].Journal of the Royal Statistical Society,1974,36(2):192-236.
[26]	CLIORD P．Markov random fields in statistics[M]// HAMMERSLEY M J, GRIMMETT G, WELSH D．Disorder in physical systems．New York: Oxford University Press, 1990: 19-32．
[27]	HONGLER C,SMIRNOV S.The energy density in the planar Ising model[J].Acta Mathematica Scientia,2013,211(2):191-225. doi: 10.1007/s11511-013-0102-1
[28]	WEGENER I．Simulated annealing beats metropolis in combinatorial optimization[M]// BUGLIESI M, PRENEEL B, SASSONE V, et al．Automata languages and programming．Berlin Heidelberg: Springer-Verlag, 2005: 589-601．
[29]	RUZANSKI E,CHANDRASEKAR V,WANG Y.The CASA nowcasting system[J].Journal of Atmospheric and Oceanic Technology,2011,28(5):640-655. doi: 10.1175/2011JTECHA1496.1
[30]	NI K S,NGUYEN T Q.An adaptable k-nearest neighbors algorithm for MMSE image interpolation[J].IEEE Transactions on Image Processing,2009,18(9): 1976 - 1987. doi: 10.1109/TIP.2009.2023706
[31]	KIM K H,SHIM P S,SHIN S.An alternative bilinear interpolation method between spherical grids[J].Atmosphere,2019,10(3):123. doi: 10.3390/atmos10030123
[32]	LETIZIA S,ZHAN L,IUNGO G V.LiSBOA:LiDAR statistical Barnes objective analysis for optimal design of LiDAR scans and retrieval of wind statistics. Part I:theoretical framework[J].Atmospheric Measurement Techniques,2021,14(3): 2065 - 2093. doi: 10.5194/amt-14-2065-2021