Using DGPS as An Acceleration Sensor for Airborne Gravimetry

  • Zhang, Kaidong (Mechatronics and Automation School, National University of Defense Technology) ;
  • Shen, Lincheng (Mechatronics and Automation School, National University of Defense Technology) ;
  • Hu, Xiaoping (Mechatronics and Automation School, National University of Defense Technology) ;
  • Wu, Meiping (Mechatronics and Automation School, National University of Defense Technology)
  • 발행 : 2006.10.18

초록

In airborne gravimetry, there are two data streams. One is the specific force measured by an air/sea gravimeter or accelerometers, the other is kinematic acceleration measured by DGPS. And the difference of them provides the gravity disturbance information. To satisfy the requirement of most applications, an accuracy of 1mGal $(1mCal=10^{-5}m/s^{2})$ with a spatial resolution of 1km is the aim of current airborne gravimetry. There are two different methods to derive the kinematic acceleration. The generally used method is to differentiate the position twice, and the position can be calculated by commercial DGPS software. The main defect of this method is that integer ambiguities need to be fixed to get the precise position solution, but it's not a trivial thing for long base line. And to fix integer ambiguities, the noisier iono-free measurement is used. When differentiation is applied, noise is amplified and will influence the accuracy of acceleration. The other method is to get carrier phase acceleration by differentiate the carrier phase first, and then using the acceleration of GPS satellite to derive the vehicle acceleration. The main advantages include that fixing integer ambiguities is not needed anymore, position can be relaxed to about 10 meters, and smoother acceleration can be got since iono-free measurement is not needed. In some literatures, it's considered that the dynamic performance of the second method is inferior to that of the first. Through analysis, it is found that the performance degradation in dynamic environment results from the simplification of the GPS carrier phase observable model. And an iterative algorithm is presented to compensate the model error. Using a dynamic GPS data from an aeromagnetic survey, the importance of this compensation is showed at last.

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