- freely available
An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression
AbstractMicro 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.
Share & Cite This Article
Bhatt, D.; Aggarwal, P.; Bhattacharya, P.; Devabhaktuni, V. An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression. Sensors 2012, 12, 9448-9466.View more citation formats
Bhatt D, Aggarwal P, Bhattacharya P, Devabhaktuni V. An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression. Sensors. 2012; 12(7):9448-9466.Chicago/Turabian Style
Bhatt, Deepak; Aggarwal, Priyanka; Bhattacharya, Prabir; Devabhaktuni, Vijay. 2012. "An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression." Sensors 12, no. 7: 9448-9466.
Notes: Multiple requests from the same IP address are counted as one view.