Systematic Error Compensation for Airborne Gravimetry

2013 ◽  
Vol 56 (1) ◽  
pp. 1-7 ◽  
Author(s):  
SUN Zhong-Miao ◽  
ZHAI Zhen-He ◽  
XIAO Yun ◽  
LI Ying-Chun
2006 ◽  
Author(s):  
R. Rodríguez-Vera ◽  
R. R. Cordero ◽  
F. Labbe ◽  
J. A. Rayas ◽  
Amalia Martínez ◽  
...  

2015 ◽  
Vol 53 (7) ◽  
pp. 3985-3995 ◽  
Author(s):  
Yong-hua Jiang ◽  
Guo Zhang ◽  
Peng Chen ◽  
De-ren Li ◽  
Xin-ming Tang ◽  
...  

2014 ◽  
Vol 621 ◽  
pp. 519-524
Author(s):  
Min Ye ◽  
Xiang Jun Zou ◽  
Jun Tao Xiong ◽  
Hong Jun Wang ◽  
Yan Chen ◽  
...  

Target stereoscopic positioning is a significant sign of intelligent robot. The paper first described the research status of harvesting robot target stereoscopic positioning, discussed senor and stereoscopic positioning applications. Then, method and algorithm of rapid identification in target image were stated. In addition, version positioning, mechanism positioning, comprehensive positioning, associated positioning error, random positioning systematic error and application and existing problem of robot were introduced in this paper. Finally, harvesting robot positioning error compensation was proposed with the aiming at realizing robot precision positioning and expecting a great prospect of intelligent harvesting robot.


2015 ◽  
Vol 713-715 ◽  
pp. 500-503
Author(s):  
Xiao Wei Dai ◽  
Yu Zhen Xie

Environmental function matrix computing can provide aircraft data support for guidance instrument systematic error compensation to promote the precision. There were two ways to calculate the environmental function matrix, telemetry data direct computing method and telemetry data iterative computing method. A new iterative computing method with tracing data was put forward. The three methods were compared by using simulated data to do the experiment .It could be concluded that the precision of the tracing data iterative computing method was the best of three methods.


Measurement ◽  
2015 ◽  
Vol 73 ◽  
pp. 473-479 ◽  
Author(s):  
Ryota Kudo ◽  
Kenya Okita ◽  
Kohei Okuda ◽  
Yusuke Tokuta ◽  
Motohiro Nakano ◽  
...  

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