Mimu/GPS/Magnetometer Integrated Navigation Sequential Adaptive Filtering Technology

As the rapid development of MEMS technology, Micro-Inertial Measurement Unit (MIMU) becomes the new and popular research field at home and abroad for the advantages of low-cost and small size. This paper is set up on the background of the application of MIMU/GPS/Magnetometer integrated system. Sequential process and adaptive filter technology based on sequential structure are studied to apply into the integrated system. The main work and innovation points include the following aspects:1,The 15-dimension filtering model of MIMU/GPS/Magnetometer integrated system is deduced and built. Designing principles of the adaptive filter are briefly stated. Two abnormal error detection methods which are applied into the Kalman filter are analyzed and researched. Kalman filter sequential process is researched and it is applied into integrated system. A new information fusion strategy is correspondingly design to sufficiently utilize the information of GPS and magnetometer. Simulation and vehicle experiments show that the filtering results of sequential process are better than the standard Kalman filtering on the condition of low sensors'data output frequency.2,The adaptively robust filter is designed based on the sequential structure. First, three kinds of single-adaptively robust filters are analyzed and studied. The fading adaptive filter and adaptively robust filter based on residue are comparatively researched. Some innovations are made in the adaptively robust filter based on residue to increase its robustness. Then complex adaptively robust filter can also be realized based on the sequential structure, which can suppress the abnormal interferences simultaneously or sequentially. Finally, the influence caused by the vehicle maneuver is analyzed, and the correspondingly maneuver detection and avoiding measure has been designed.3,The Sage-Husa adaptive filter of sequential structure is studied and applied into MIMU/GPS/Magnetometer integrated system. The characters of Sage-Husa adaptive filter are analyzed. This filter is simplified for engineering application. Two equivalent estimation methods for the measurement noise matrix Rk are given and verified. The covariance matching is introduced into Sage-Husa adaptively robust filter to test its robustness. The simplified Sage-Husa adaptive filter is applied into integrated system. From the results of experiment, we know that the effect of adaptive filtering is slightly influenced by the quantity of the information of measurement noise. The utilization rate of sensors'output information is the main factor deciding the filtering effect. These experiments also show that, Sage-Husa adaptive robust filter don't do well in suppressing the abnormal interferences. Comparing with the adaptively robust filters mentioned above, Sage-Husa adaptive filter has some shortages in robustness and convenience to realize in engineering.