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Proceedings Paper

Aided position method based on gravity gradient full tensor fusion matching
Author(s): Lin-wei Xiao; Kui-sheng Chen; Bin-bin Dan; Ling Xiong; Jie Ma
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Paper Abstract

Gravity gradient is a tensor with five mutual independent components. Five gravity gradient components are complementary. Combining the gravity gradient full tensor, more detail information is contributed to gravity gradient matching aided position. Gravity gradient full tensor fusion matching aided position method is proposed in this paper. The matching strategy is particle filtering (PF) and fusion strategy is weighted fusion on the confidence coefficient of each gravity gradient component. Simulations have been done and results showed that full tensor fusion matching aided position method is more effective than the aided position method based on single gravity gradient component.

Paper Details

Date Published: 26 October 2013
PDF: 8 pages
Proc. SPIE 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 89211B (26 October 2013); doi: 10.1117/12.2031318
Show Author Affiliations
Lin-wei Xiao, Wuhan Univ. of Science and Technology (China)
Kui-sheng Chen, Wuhan Univ. of Science and Technology (China)
Bin-bin Dan, Wuhan Univ. of Science and Technology (China)
Ling Xiong, Wuhan Univ. of Science and Technology (China)
Jie Ma, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 8921:
MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Jinwen Tian; Jie Ma, Editor(s)

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