Analysis and modelling of MEMS inertial measurement unit

Thanks to advances in the development of Micro-Electromechanical Systems (MEMS), it has been possible to fabricate small dimension and cheap accelerometers and gyros, which are being used in many applications where the GPS/INS integration is carried out, as for example to identify track defects, navigation, geo-referencing, agriculture, etc. Although these MEMS devices have a low-cost, they present different errors which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modelling of these errors is necessary in order to improve the system performance. In this work, Allan Variance and Power Spectral Density techniques are used to identify the random processes that affect the inertial sensor data. Once the random components are identified, they are modelled using first-order Gauss-Markov and random walk processes. Two models are assessed augmenting the states of the Extended Kalman Filter (EKF) to 6 and 9. Subsequently, another analysis and modelling of the inertial sensors which combines Autoregressive Filters and Wavelet De-noising is implemented and in this case the EKF of the loosely coupled GPS/INS integration strategy is augmented with 6, 12 and 18 states. Finally, the results show a comparison between these sensor error models with real data under GPS outage conditions.