Self-adaptive zero velocity update pedestrian navigation method based on MEMS sensor

Abstract

The invention discloses a self-adaptive zero velocity update pedestrian navigation method based on a MEMS sensor. By analyzing gait during people multidirectional movement, based on output of the MEMSsensor under different motion modals, a zero velocity update discrimination algorithm combines with strapdown inertial navigation resolving for correcting information radiated with time of speed position resolved by correction resolution; a self-adaptive zero velocity correction model is established according to practical motion characteristic of pedestrians, and zero velocity discrimination threshold can be changed with real time. The method better solve the radiation problem of speed position of different pedestrians under different motion modals under pure inertia resolving, and self-adaptability and reliability of pedestrian navigation positioning during people multidirectional movement is increased.

Description

technical field [0001] The invention relates to the technical field of integrated navigation, in particular to an adaptive zero-speed correction pedestrian navigation method based on MEMS sensors. Background technique [0002] Pedestrian positioning and navigation, as one of the main areas of civil navigation technology development in recent years, is gradually being paid attention to and researched. The existing pedestrian navigation research directions can be mainly divided into the following two categories: one is source positioning based on various wireless networks, but this type of method relies on additional equipment (such as WIFI, Bluetooth, UWB, etc.), and the positioning accuracy is greatly affected by the environment. Susceptible to interference; the second is pedestrian navigation and positioning based on MEMS sensors, with inertial devices as the core, featuring short-term high precision and high stability, and is a completely autonomous navigation system. Acc...

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Problems solved by technology

The heading error calculated by the strapdown is also relatively large, and it is prone to fluctuations, resulting in a discrepancy between the navigation result and the actual route. Effective correction must be done to improve the accuracy of the navigation result

Pedestrians have a variety of motion modes in the actual motion process, such as fast walking, running, up and down stairs, jumping, etc. At the same time, there are also large differences in the gaits of different people during the motion process, making it difficult to detect and distinguish gaits ; while the existing research at home and abroad mainly focuses on the identification of pedestrians' normal walking modes, and there is little mention of fast walking, running, going up and down stairs, jumping, etc.

At the same time, the existing zero-speed detection methods cannot adapt to the defects of different pedestrian gaits and different motion modes based on different devices, and more in-depth research is needed

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