On gravity disturbance compensation technology of high-precision SINS based on B-spline method
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: 2014DOI: 10.1109/ICMA.2014.6885980
Keyword(s): backpropagation,n computer vision,n feature extraction,n image motion analysis,n image texture,n neural nets,n object recognition,n principal component analysis,n target tracking,n 3D ship models,n BP neural network,n PCA,n backpropagation neural network,n combined features,n contour features,n data source,n feature extraction method,n feature recognition method,n geometric features,n hand-making remote vessel,n image capture,n milit
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On gravity disturbance compensation technology of high-precision SINS based on B-spline method
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| contributor author | Cong, Lin | |
| contributor author | Zhao, Zhong | |
| contributor author | Yang, Xiaobu | |
| date accessioned | 2020-03-12T22:23:47Z | |
| date available | 2020-03-12T22:23:47Z | |
| date issued | 2014 | |
| identifier other | 7007213.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1081577 | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | On gravity disturbance compensation technology of high-precision SINS based on B-spline method | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8218078 | |
| subject keywords | backpropagation | |
| subject keywords | n computer vision | |
| subject keywords | n feature extraction | |
| subject keywords | n image motion analysis | |
| subject keywords | n image texture | |
| subject keywords | n neural nets | |
| subject keywords | n object recognition | |
| subject keywords | n principal component analysis | |
| subject keywords | n target tracking | |
| subject keywords | n 3D ship models | |
| subject keywords | n BP neural network | |
| subject keywords | n PCA | |
| subject keywords | n backpropagation neural network | |
| subject keywords | n combined features | |
| subject keywords | n contour features | |
| subject keywords | n data source | |
| subject keywords | n feature extraction method | |
| subject keywords | n feature recognition method | |
| subject keywords | n geometric features | |
| subject keywords | n hand-making remote vessel | |
| subject keywords | n image capture | |
| subject keywords | n milit | |
| identifier doi | 10.1109/ICMA.2014.6885980 | |
| journal title | uidance, Navigation and Control Conference (CGNCC), 2014 IEEE Chinese | |
| filesize | 125327 | |
| citations | 0 |