Implementation of identification system for IMUs based on Kalman Filtering

Abstract

Modeling and simulation studies are used to measure the desired performance prior to the hardware implementation of inertial navigation systems. Inertial measurement units are the main components of the inertial navigation systems. Therefore, IMUs should be modeled within the scope of modeling and simulation studies of inertial navigation systems. Several time and frequency domain analysis are implemented in these simulation studies. In addition to deterministic and stochastic error parameters, frequency and delay characteristics of the sensors required for inertial sensor identification. Hence, transfer functions of accelerometer and gyroscope channels are required. Generally, transfer functions of COTS IMUs, accelerometers and gyroscopes are not provided to end-users. Therefore, identification of sensor transfer functions becomes a problem. In order to identify sensor transfer function several methods have been examined. This study explains the how the transfer functions of inertial sensors are defined by using system identification with Kalman Filter. System identification deals with the problem of building mathematical models of dynamical systems based on observed data from the system. System identification consists of data record, generating of model set and determining of the best model steps and lots of several methods can be used in these steps. In the scope of this study Kalman Filter is used to generate candidate transfer function set in the generating of model set step of the system identification. Transfer function identification process will be completed by selecting the best model from the model set. Thereby, effects of frequency and delay characteristics on the system performance can be observed. An IMU can be modeled in frequency domain with transfer function by using the methodology which is explained in this study.