An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression
Open Access
- 9 July 2012
- Vol. 12 (7), 9448-9466
- https://doi.org/10.3390/s120709448
Abstract
Micro Electro Mechanical System (MEMS)-based inertial sensors have made possible the development of a civilian land vehicle navigation system by offering a low-cost solution. However, the accurate modeling of the MEMS sensor errors is one of the most challenging tasks in the design of low-cost navigation systems. These sensors exhibit significant errors like biases, drift, noises; which are negligible for higher grade units. Different conventional techniques utilizing the Gauss Markov model and neural network method have been previously utilized to model the errors. However, Gauss Markov model works unsatisfactorily in the case of MEMS units due to the presence of high inherent sensor errors. On the other hand, modeling the random drift utilizing Neural Network (NN) is time consuming, thereby affecting its real-time implementation. We overcome these existing drawbacks by developing an enhanced Support Vector Machine (SVM) based error model. Unlike NN, SVMs do not suffer from local minimisation or over-fitting problems and delivers a reliable global solution. Experimental results proved that the proposed SVM approach reduced the noise standard deviation by 10–35% for gyroscopes and 61–76% for accelerometers. Further, positional error drifts under static conditions improved by 41% and 80% in comparison to NN and GM approaches.Keywords
This publication has 16 references indexed in Scilit:
- Modeling and compensation of MEMS gyroscope output data based on support vector machineMeasurement, 2012
- Calibration and Stochastic Modelling of Inertial Navigation Sensor ErrosThe Journal of Global Positioning Systems, 2008
- Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation ApplicationsIEEE Transactions on Vehicular Technology, 2008
- Error and Performance Analysis of MEMS-based Inertial Sensors with a Low-cost GPS ReceiverSensors, 2008
- Analysis and Modeling of Inertial Sensors Using Allan VarianceIEEE Transactions on Instrumentation and Measurement, 2007
- Modeling the Random Drift of Micro-Machined Gyroscope with Neural NetworkNeural Processing Letters, 2005
- A tutorial on support vector regressionStatistics and Computing, 2004
- An Introduction to Microelectromechanical Systems EngineeringMeasurement Science and Technology, 2002
- New Support Vector AlgorithmsNeural Computation, 2000
- Statistics of atomic frequency standardsProceedings of the IEEE, 1966