Study of Grey Model Theory and Neural Network Algorithm for Improving Dynamic Measure Precision in Low Cost IMU
Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.980
The sensors' output data must be optimized because of the zero output varies along with time and temperature in the dynamic measuring accuracy of low cost inertial measurement unit (IMU). Two steps are done to achieve the designed precision. Firstly, the Grey model theory is proposed for the gyro's null drift output data process. Secondly, the RBF neural network is presented to compensate the gyro's null drift. Experiment proved that the mean variance of the zero drifting depresses from 0.0086 to 0.0004
inertial measurement unit, Grey model, RBF neural network, compensation algorithm
P. Yingjun, L. Leilei, L. Dengfeng, L. Yu, S. Yanbin and L. Jun, "Study of Grey Model Theory and Neural Network Algorithm for Improving Dynamic Measure Precision in Low Cost IMU," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 234-238.