A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units

Abstract

Navigation involves the integration of methodologies and systems for estimating the time varying position and attitude of moving objects. Inertial Navigation Systems (INS) and the Global Positioning System (GPS) are among the most widely used navigation systems. The use of cost effective MEMS based inertial sensors has made GPS/INS integrated navigation systems more affordable. However MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable navigation results. Moreover the performance characteristics of these sensors are highly dependent on the environmental conditions such as temperature variations. Hence there is a need for the development of accurate, reliable and efficient thermal models to reduce the effect of these errors that can potentially degrade the system performance. In this paper, the Allan variance method is used to characterize the noise in the MEMS sensors. A six-position calibration method is applied to estimate the deterministic sensor errors such as bias, scale factor, and non-orthogonality. An efficient thermal variation model is proposed and the effectiveness of the proposed calibration methods is investigated through a kinematic van test using integrated GPS and MEMS-based inertial measurement unit (IMU).

Role of entanglement in calibrating optical quantum gyroscopes

“…If we then wish to relate the gyroscope orientation to an external reference frame we need another three parameters, and this can be achieved by taking another set of measurements at a fourth orientation. This analysis implies that the generalization of our analysis to three dimensions does not pose a major obstacle-which is to be expected since similar processes are already used in inertial navigation systems and can include more complicated sequences of rotations and applied rotation rates to characterize other errors [30,31]-and there is no reason to believe that the main results given above for entangled input states would simplify significantly in moving from two to three dimensions. If anything, the optimization of the input states in three dimensions would be expected to be an even more complicated problem than the case presented here.…”

An Optimal Calibration Method for a MEMS Inertial Measurement Unit

“…For each cluster time T, the ensemble of s ξ defined by (5) forms a set of random variables. The quantity of interest is the variance of s ξ over all the clusters of the same size that can be formed from the entire data.…”

INS/GNSS/Vehicle Speed Integration for Land Vehicles with Utilizing Zero-Velocity Information

“…However, the MEMS IMU outputs contain much higher level errors such as high frequency noises, drift errors and scale factor errors, and they are accumulated with time by the integrating calculations of INS (Inertial Navigation System). Therefore, as well known, the integration with the external measurement of the position and velocity by GNSS (Global Navigation Satellite System) which has the characteristic of the long term stability is one of the important keys to hold the accurate navigation [5,6]. However, the navigation errors very quickly grow when GNSS is unavailable.…”