| Summary: | 碩士 === 國立交通大學 === 土木工程系 === 88 === There are many successful examples in using of INS/GPS in the world, mostly apply to military and navigation. In short baseline, the precision of Kinematic GPS and Real-time kinematic (RTK) have already reached to the level of centimeter. The price of high precision INS instrument is expensive. To use cheaper INS instrument for positioning when GPS surveying is not work. So the integrating GPS with INS avoid the defects of each system, and raise the accuracy of positioning results.
In this study, two simulative INS data and data collected from the Center for Mapping at the Ohio State University is applied for positioning. In addition, the decentralized Kalman Filter and 15 parameters INS model for data processing is used. Moreover, the data processing procedure of the Kalman Filter is modified to get automatic and real time positioning object.
From the results, it was found that using divide data and adjust the Kalman Filter system noise covariance matrix automatically can combine both system into the optimum state. During the intentional gaps of GPS observation of the OSU practical data, the difference of INS position and KGPS solution reaches 35cm within 20 seconds. But it use more time to compute. On the other hand, use two different system noise covariance matrix which has GPS signal or not, the result batter then use a system noise covariance matrix with GPS signal or not.
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