Study On Personal Navigation Algorithms Based On MEMS-INS/GPS Integrated

In recent years, with the rapid development of large data, networking, positioning and navigation based on personal service have attracted much attention in the military, business, science and other fields. It has become the currently the main direction of the research of navigation and location. As MEMS inertial device more and more miniaturization, integration, personal navigation of MEMS has been favored by the majority of scholars. As is known to all, using GPS positioning, largely restricted by the environment, in tunnel, forest and in indoor environment, it is often unable to provide services. However, INS is not affected by the environment to realize autonomous positioning. With the increase of the navigation time, INS often brings the cumulative errors of position and course, which makes the navigation system become worse.In order to better realize the pedestrian navigation and positioning, this subject adopts the Dutch XSENS MTI-G-700 as main research object to study the personal navigation system based on MEMS-INS/GPS combination and applicable to in the city populated area, shopping malls, residential area and other places of indoor and outdoor perimeter, police and firefighters working in the field of high risk, etc. It is used to provide more accurate location information and to better to provide security for the positioning personnel, moreover in senior housing, nursing homes, prisons and other places personal navigation system also can develop the function of navigation and positioning.In order to restrain the growth of INS error with the integral effect, according to the law of the human gait characteristics this paper presents a multi gait zero speed detection method based on pedestrian. This method has more accurate zero speed detection for the stairs, down the steps, climbing, walk, run and various gait pattern. At zero velocity moment, we use kalman zero velocity correction algorithm to estimate the error, and real-time feedback correction. When GPS effectively, this article adopts the method of UKF in INS/GPS integrated navigation, analysis and contrast the EKF and UKF filter effect. At the same time, because of the heading angle cannot be observed in kalman zero velocity correction phase, the error cannot be revised. At this point, this paper uses the GPS position information of the linear stage to calculate the heading, to correct the accumulated heading error. When GPS is unavailable, in order to avoid heading drift, using Google Earth to get the outline of the building information, through MapInfo for vector to get the heading information of the outline of the building. In the linear stage, the heading information of the outline of the building is used to inhibit the drift of the heading angle.The MEMS sensor MTI-G-700 is bundled on vamp, on campus in a number of places and building test. The effectiveness of the navigation algorithm is verified through a lot of experiments. The experimental results show that the multi gait zero speed detection has a good effect on zero speed detection in the condition of climbing stairs, walking, running and so on. In the process of integrated navigation, UKF is better than EKF. With the aid of the building, the heading drift is effectively suppressed, and the positioning accuracy is less than 0.3% in the experiment of 900m.