Data Fusion Method of Measurement Lag Compensation for Multi-rate MIMU/FOG/GNSS Compound Navigation

In the future, smart intelligent ammunition will be widely applied in the military field. Small volume microelectromechanical system inertial measurement units (MIMUs) have become the mainstream solution for a smart intelligent ammunition navigation system; however, the accuracy of the IMU is the main factor limiting the accuracy of the strikes. To improve the navigation accuracy of the MIMU, various integrated methods have been proposed, for example, the integration of a global navigation satellite system (GNSS) and a magnetometer. In this study, a compound navigation system (CPNS) is designed to improve the navigation accuracy of the MIMU. The CPNS consists of an oblique single-axis fiber optic gyroscope (FOG), a MIMU and a GNSS. The proposed method solves two problems: the calibration of the MEMS gyro and GPS measurement lag. With a FOG as the measurement, MEMS gyro errors are calibrated online by the designed Kalman filter. Thus, the accuracy of the CPNS-IMU can be improved. When GPS measurement lag occurs, the accuracy of the traditional Kalman filter will deteriorate. To reduce the data fusion error of the CPNS under measurement lag, a distributed optimal fusion estimation method is proposed based on the multi-scale theory. The hardware-in-the-loop simulation results demonstrate that the constant bias, scale factor errors, and misalignment errors of the MEMS gyro are effectively suppressed by configuring the FOG to the MIMU. The navigation error of the CPNS-IMU is reduced by more than 50% as compared with the navigation error of the MIMU. In the CPNS with the measurement lag, the fusion error is reduced by approximately 33% in the proposed method as compared with the existing methods.