| 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)
|
Geomagnetic matching navigation technology is an important auxiliary navigation guidance method. The construction accuracy of geomagnetic reference map plays a decisive role in the accuracy of geomagnetic matching guidance. Aimed at the problem that the construction accuracy of the existing geomagnetic reference maps is difficult to meet the actual requirements of geomagnetic matching navigation, a construction method of geomagnetic reference maps based on convolutional neural network is proposed. First, the convolutional layer is used to extract the feature image patches in the low-resolution reference image. Then, the Learned Iterative Shrinkage and Thresholding Algorithm (LISTA) is used to realize the sparse representation of the low-resolution image patches. Finally, the three-channel geomagnetic information is used to obtain the final reconstructed high-resolution reference map. The experimental results show that the proposed method has a higher construction accuracy for geomagnetic reference map and better robustness to noise. Various objective evaluation indexes of the proposed method are higher than those of the existing super-resolution reconstruction algorithms.
| [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.
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).
|
| [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.
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).
|
| [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
|
Figures(5) / Tables(6)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@cqjj8.com
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
