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一种基于卷积神经网络的地磁基准图构建方法

马啸宇 张金生 李婷

马啸宇, 张金生, 李婷等 . 一种基于卷积神经网络的地磁基准图构建方法[J]. 北京航空航天大学学报, 2021, 47(9): 1918-1926. doi: 10.13700/j.bh.1001-5965.2020.0268
引用本文: 马啸宇, 张金生, 李婷等 . 一种基于卷积神经网络的地磁基准图构建方法[J]. 北京航空航天大学学报, 2021, 47(9): 1918-1926. doi: 10.13700/j.bh.1001-5965.2020.0268
MA Xiaoyu, ZHANG Jinsheng, LI Tinget al. A geomagnetic reference map construction method based on convolutional neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(9): 1918-1926. doi: 10.13700/j.bh.1001-5965.2020.0268(in Chinese)
Citation: MA Xiaoyu, ZHANG Jinsheng, LI Tinget al. A geomagnetic reference map construction method based on convolutional neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(9): 1918-1926. doi: 10.13700/j.bh.1001-5965.2020.0268(in Chinese)

一种基于卷积神经网络的地磁基准图构建方法

doi: 10.13700/j.bh.1001-5965.2020.0268
基金项目: 

国家自然科学基金 61673017

中国博士后科学基金 2019M3643

详细信息
    通讯作者:

    张金生, E-mail: 15309217656@163.com

  • 中图分类号: V221+.3;TB553

A geomagnetic reference map construction method based on convolutional neural network

Funds: 

National Natural Science Foundation of China 61673017

China Postdoctoral Science Foundation 2019M3643

More Information
  • 摘要:

    地磁匹配导航技术是一种重要的辅助导航制导方法,地磁基准图的构建精度对地磁匹配制导的精准度起着决定性作用。针对现有地磁基准图构建精度难以满足实际地磁匹配导航需求的问题,提出了一种基于卷积神经网络的地磁基准图构建方法。首先,利用卷积层提取低分辨率基准图中的特征图像块;然后,利用基于学习的阈值收缩算法(LISTA)实现图像块的稀疏表示;最后,利用三通道的地磁信息得到重建后的高分辨率基准图。实验结果表明:所提方法对地磁基准图具有更高的构建精度,同时对噪声有更好的鲁棒性,各种客观评价指标均高于现有的超分辨率重建方法。

     

  • 图 1  地磁参考场特征

    Figure 1.  Features of geomagnetic reference field

    图 2  ISTA网络结构

    Figure 2.  Structure of ISTA network

    图 3  本文网络模型整体结构

    Figure 3.  Overall structure of the proposed network model

    图 4  本文方法流程

    Figure 4.  Flowchart of the proposed method

    图 5  4倍放大下地磁基准图重建效果对比

    Figure 5.  Comparison of geomagnetic reference map reconstructed with magnification factor 4

    表  1  2倍放大下不同方法PSNR、SSIM和RMSE指标对比

    Table  1.   Comparison of PSNR, SSIM and RMSE of different methods with magnification factor 2

    方法 PSNR/dB SSIM RMSE/nT
    双三次插值 30.56 0.801 3.02
    稀疏编码 31.51 0.835 3.82
    PSO-Kriging 31.44 0.831 2.93
    本文方法 31.67 0.847 2.77
    下载: 导出CSV

    表  2  3倍放大下不同方法PSNR、SSIM和RMSE指标对比

    Table  2.   Comparison of PSNR, SSIM and RMSE of different methods with magnification factor 3

    方法 PSNR/dB SSIM RMSE/nT
    双三次插值 27.66 0.571 8.19
    稀疏编码 28.39 0.634 7.62
    PSO-Kriging 28.18 0.623 7.88
    本文方法 28.64 0.664 7.31
    下载: 导出CSV

    表  3  4倍放大倍数下不同方法PSNR、SSIM和RMSE指标对比

    Table  3.   Comparison of PSNR, SSIM and RMSE of different methods with magnification factor 4

    方法 PSNR/dB SSIM RMSE/nT
    双三次插值 25.13 0.446 15.47
    稀疏编码 26.12 0.508 12.87
    PSO-Kriging 25.99 0.501 13.45
    本文方法 26.55 0.561 12.11
    下载: 导出CSV

    表  4  不同噪声等级下各种方法PSNR值对比

    Table  4.   Comparison of PSNR of different methods under different noise levels

    方法 PSNR/dB
    σ=0 σ=5 σ=10 σ=15 σ=20
    稀疏编码 28.39 28.31 28.22 28.13 28.05
    SRCNN 28.44 28.34 28.22 28.13 28.02
    PSO-Kriging 28.18 28.09 28.01 27.94 27.85
    本文方法 28.64 28.58 28.52 28.48 28.44
    下载: 导出CSV

    表  5  不同噪声等级下各种方法SSIM值对比

    Table  5.   Comparison of SSIM of different methods under different noise levels

    方法 SSIM
    σ=0 σ=5 σ=10 σ=15 σ=20
    稀疏编码 0.634 0.629 0.623 0.616 0.609
    SRCNN 0.642 0.634 0.627 0.620 0.611
    PSO-Kriging 0.623 0.614 0.606 0.599 0.589
    本文方法 0.664 0.661 0.657 0.652 0.648
    下载: 导出CSV

    表  6  不同噪声等级下各种方法RMSE值对比

    Table  6.   Comparison of RMSE of different methods under different noise levels

    方法 RMSE/nT
    σ=0 σ=5 σ=10 σ=15 σ=20
    稀疏编码 7.62 7.69 7.77 7.84 7.92
    SRCNN 7.45 7.58 7.69 7.78 7.89
    PSO-Kriging 7.88 7.97 8.11 8.23 8.35
    本文方法 7.31 7.37 7.42 7.47 7.55
    下载: 导出CSV
  • [1] HOLLAND R A, THORUP K, VONHOF M J, et al. Bat orientation using Earth's magnetic field[J]. Nature, 2006, 444(7120): 702. doi: 10.1038/444702a
    [2] ECKENHOFF K, GENEVA P, HUANG G Q. Direct visual-inertial navigation with analytical preintegration[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2017: 1429-1435.
    [3] CUNTZ M, KONOVALTSEV A, MEURER M. Concepts, development, and validation of multiantenna GNSS receivers for resilient navigation[J]. Proceedings of the IEEE, 2016, 104(6): 1288-1301. doi: 10.1109/JPROC.2016.2525764
    [4] LOHMANN K J, LOHMANN C M F, EHRHART L M, et al. Geomagnetic map used in sea-turtle navigation[J]. Nature, 2004, 428(6986): 909-910. doi: 10.1038/428909a
    [5] 岳建平, 甄宗坤. 基于粒子群算法的Kriging插值在区域地面沉降中的应用[J]. 测绘通报, 2012(3): 59-62. https://www.cnki.com.cn/Article/CJFDTOTAL-CHTB201203019.htm

    YUE J P, ZHEN Z K. Application of particle swarm optimization based Kriging interpolation method in regional land subsidence[J]. Bulletin of Surveying and Mapping, 2012(3): 59-62(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CHTB201203019.htm
    [6] 李晨霖, 王仕成, 张金生, 等. 基于改进的Kriging插值方法构建地磁基准图[J]. 计算机仿真, 2018, 35(12): 262-266. doi: 10.3969/j.issn.1006-9348.2018.12.062

    LI C L, WANG S C, ZHANG J S, et al. Construction of geomagnetic datum map based on improved Kriging interpolation method[J]. Computer Simulation, 2018, 35(12): 262-266(in Chinese). doi: 10.3969/j.issn.1006-9348.2018.12.062
    [7] GOLDENBERG F. Geomagnetic navigation beyond the magnetic compass[C]//2006 IEEE/ION Position, Location, and Navigation Symposium. Piscataway: IEEE Press, 2006: 684-694.
    [8] 张涛, 郑建华, 高东. 一种利用磁强计和星敏感器的自主导航方法[J]. 宇航学报, 2017, 38(2): 152-158. doi: 10.3873/j.issn.1000-1328.2017.02.006

    ZHANG T, ZHENG J H, GAO D. A method of autonomous navigation using the magnetometer and star sensor[J]. Journal of Astronautics, 2017, 38(2): 152-158(in Chinese). doi: 10.3873/j.issn.1000-1328.2017.02.006
    [9] 华冰, 张志文, 王峰, 等. 基于地磁/光谱红移/太阳光信息的FAUKF自主定轨[J]. 系统工程与电子技术, 2019, 41(1): 154-161. https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201901022.htm

    HUA B, ZHANG Z W, WANG F, et al. FAUKF autonomous orbit determination based on geomagnetic/spectral redshift/sunlight information[J]. Systems Engineering and Electronics, 2019, 41(1): 154-161(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201901022.htm
    [10] CAI Q Z, YANG G L, SONG N F, et al. Analysis and calibration of the gyro bias caused by geomagnetic field in a dual-axis rotational inertial navigation system[J]. Measurement Science and Technology, 2016, 27(10): 105001. doi: 10.1088/0957-0233/27/10/105001
    [11] LIU M Y, LIU K, YANG P P, et al. Bio-inspired navigation based on geomagnetic[C]//2013 IEEE International Conference on Robotics and Biomimetics (ROBIO). Piscataway: IEEE Press, 2013: 2339-2344.
    [12] 杨宇翔. 图像超分辨率重建算法研究[D]. 合肥: 中国科学技术大学, 2013: 14-28.

    YANG Y X. Image super resolution reconstruction[D]. Hefei: University of Science and Technology of China, 2013: 14-28(in Chinese).
    [13] WANG Z Y, YANG Y Z, WANG Z W, et al. Learning super-resolution jointly from external and internal examples[J]. IEEE Transactions on Image Processing, 2015, 24(11): 4359-4371. doi: 10.1109/TIP.2015.2462113
    [14] DENG C, XU J, ZHANG K B, et al. Similarity constraints-based structured output regression machine: An approach to image super-resolution[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(12): 2472-2485. doi: 10.1109/TNNLS.2015.2468069
    [15] LI X, ORCHARD M T. New edge-directed interpolation[J]. IEEE Transactions on Image Processing, 2001, 10(10): 1521-1527. doi: 10.1109/83.951537
    [16] WEN B H, RAVISHANKAR S, BRESLER Y. Structured overcomplete sparsifying transform learning with convergence guarantees and applications[J]. International Journal of Computer Vision, 2015, 114(2-3): 137-167. doi: 10.1007/s11263-014-0761-1
    [17] IRANI M, PELEG S. Improving resolution by image registration[J]. CVGIP: Graphical Models and Image Processing, 1991, 53(3): 231-239. doi: 10.1016/1049-9652(91)90045-L
    [18] YANG J C, LIN Z, COHEN S. Fast image super-resolution based on in-place example regression[C]//2013 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2013: 1059-1066.
    [19] YANG J C, WANG Z W, LIN Z, et al. Coupled dictionary training for image super-resolution[J]. IEEE Transactions on Image Processing, 2012, 21(8): 3467-3478. doi: 10.1109/TIP.2012.2192127
    [20] DONG C, LOY C C, HE K M, et al. Learning a deep convolutional network for image super-resolution[C]//European Conference on Computer Vision. Berlin: Springer, 2014: 184-199.
    [21] KIM J, LEE J K, LEE K M. Deeply-recursive convolutional network for image super-resolution[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2016: 1637-1645.
    [22] MACKAY D J C. Good error-correcting codes based on very sparse matrices[J]. IEEE Transactions on Information Theory, 1999, 45(2): 399-431. doi: 10.1109/18.748992
    [23] LIU D, WANG Z W, WEN B H, et al. Robust single image super-resolution via deep networks with sparse prior[J]. IEEE Transactions on Image Processing, 2016, 25(7): 3194-3207. doi: 10.1109/TIP.2016.2564643
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出版历程
  • 收稿日期:  2020-06-16
  • 录用日期:  2020-08-07
  • 网络出版日期:  2021-09-20

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