An Innovative Strategy for Accurate Thermal Compensation of Gyro Bias in Inertial Units by Exploiting a Novel Augmented Kalman Filter

Abstract

This paper presents an innovative model for integrating thermal compensation of gyro bias error into an augmented state Kalman filter. The developed model is applied in the Zero Velocity Update filter for inertial units manufactured by exploiting Micro Electro-Mechanical System (MEMS) gyros. It is used to remove residual bias at startup. It is a more effective alternative to traditional approach that is realized by cascading bias thermal correction by calibration and traditional Kalman filtering for bias tracking. This function is very useful when adopted gyros are manufactured using MEMS technology. These systems have significant limitations in terms of sensitivity to environmental conditions. They are characterized by a strong correlation of the systematic error with temperature variations. The traditional process is divided into two separated algorithms, i.e., calibration and filtering, and this aspect reduces system accuracy, reliability, and maintainability. This paper proposes an innovative Zero Velocity Update filter that just requires raw uncalibrated gyro data as input. It unifies in a single algorithm the two steps from the traditional approach. Therefore, it saves time and economic resources, simplifying the management of thermal correction process. In the paper, traditional and innovative Zero Velocity Update filters are described in detail, as well as the experimental data set used to test both methods. The performance of the two filters is compared both in nominal conditions and in the typical case of a residual initial alignment bias. In this last condition, the innovative solution shows significant improvements with respect to the traditional approach. This is the typical case of an aircraft or a car in parking conditions under solar input.

Recording the Magnetic Field Produced by an Undersea Energy Generating Device: A Low-Cost Alternative

“…One means of reducing the error in the temperature drift of the sensors is to optimize the sensor structure [60], although this greatly increases the cost and weight of the device. Another means is to identify the temperature behaviour, which can improve the accuracy of the system by establishing the temperature drift model of the sensor.…”

Modification of MTEA-Based Temperature Drift Error Compensation Model for MEMS-Gyros

“…Hence, a precise test for TDE should meet the equation given in Equation (20), which defines ambient temperature variation in precise test for TDE. In that case, MEMS-gyros are installed on a precise rate table in thermal chamber.…”

Temperature Drift Compensation for Four-Mass Vibration MEMS Gyroscope Based on EMD and Hybrid Filtering Fusion Method

“…The second solution is a software compensation method that takes as its input the relationship between the temperature of the MEMS gyroscope and the output signal and then establishes a temperature drift model to compensate for the effects of temperature drift. For example, Fontanella proposed a new method for accurate thermal compensation of the inertial device gyro bias using an augmented state Kalman filter [ 15 ]. Cao proposed a fusion algorithm based on an RBF NN-GA-KF, which effectively reduced the effect of the temperature drift on the MEMS vibration gyroscope [ 16 ].…”