Motion fault detection and isolation in Body Sensor Networks
Pervasive and Mobile Computing
| Hi-index | 0.00 |
It's well known that the accuracy of the inertial navigation systems will rapidly degrades with time because of the measure sensor's error. Several variance techniques have been devised for the error modelling of this error by way of weighting functions, PSD, ARMA and NNs, etc. In this paper, we use the SVM(support vector machine) technique to predict the future noise coming from the measure sensors especially the gyro. Then we compare the resulting noise data with the one coming from the ARMA model and NNs model. Finally the three models are compensated to the output data from the IMU to compute the position errors and attitude angle errors. The results indicate that the SVR model (support vector regression) shows more stable feature and is more adequate for long time navigation than the AR model and NNs model.