NDLI: Study of Grey Model Theory and Neural Network Algorithm for Improving Dynamic Measure Precision in Low Cost IMU

Content Provider	IEEE Xplore Digital Library
Author	Liu Yu Liu Jun Li Dengfeng Li Leilei Sun Yanbin Pan Yingjun
Copyright Year	2009
Abstract	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 and the deviation is only 30.8% of original sampled data, when the new error compensation algorithm is applied. The compensating algorithm raises the measure precision of IMU, whose static accuracy reaches to ±0.1º and dynamic accuracy is 1º (rms), and the cost is low.
Starting Page	234
Ending Page	238
File Size	430499
Page Count	5
File Format	PDF
ISBN	9780769535074
DOI	10.1109/CSIE.2009.980
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accelerometers RBF neural network Heuristic algorithms Grey model Data engineering Time measurement Sensor systems Temperature sensors compensation algorithm inertial measurement unit Neural networks Digital signal processing Cost function Solids
Content Type	Text
Resource Type	Article

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