Aided position method based on gravity gradient full tensor fusion matching

Lin-wei Xiao; Kui-sheng Chen; Bin-bin Dan; Ling Xiong; Jie Ma

doi:10.1117/12.2031318

26 October 2013 Aided position method based on gravity gradient full tensor fusion matching

Lin-wei Xiao, Kui-sheng Chen, Bin-bin Dan, Ling Xiong, Jie Ma

Proceedings Volume 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 89211B (2013) https://doi.org/10.1117/12.2031318
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

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.

Citation Download Citation

Lin-wei Xiao, Kui-sheng Chen, Bin-bin Dan, Ling Xiong, and Jie Ma "Aided position method based on gravity gradient full tensor fusion matching", Proc. SPIE 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 89211B (26 October 2013); https://doi.org/10.1117/12.2031318

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