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.
Highlights
Micro Electro Mechanical System (MEMS)-based inertial sensors have been embraced by the auto industry in their quest to improve performance, reduce cost and to enhance the reliability of the vehicles [1]
Minimization principle (ERM), Support Vector Machine (SVM) are based on the Structural Risk Minimization (SRM) principle and try to control the upper bound of generalization risk while reducing the model complexity
This paper introduces an enhanced approach based on a Nu-Support Vector Regression (Nu-SVR) technique to model the MEMS errors
Summary
Micro Electro Mechanical System (MEMS)-based inertial sensors (accelerometers and gyroscopes) have been embraced by the auto industry in their quest to improve performance, reduce cost and to enhance the reliability of the vehicles [1]. For seamless and continuous navigation solution from MEMS sensors, the modeling of errors and their reliable estimation or compensation is mandatory We solve this challenge by developing advance error models based on support vector machines. Artificial intelligence approaches utilizing Neural Network (NN) have been utilized in modeling the MEMS error and are found to perform better than other conventional techniques [14,15] In this particular case, NN suffers from poor generalization capability due to the presence of an elevated level of noises in the input-output data to be modeled. This paper proposes the implementation of an enhanced Nu-Support Vector Regression (Nu-SVR) technique for modeling these random and substantial MEMS sensor errors [19].
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