Indoor localization method based on foot inertia sensor

Abstract

The invention discloses an indoor localization method based on a foot inertia sensor. According to the indoor localization method, a framework of a grid cell path integrating method integrated and simulated on the basis of extended Kalman filter estimation is used; the information of localization positions and directions is obtained by calculating data of the inertia sensor; a plurality of condition judging criteria are used by zero-velocity correction for judging whether the feet touch the ground or not; the current velocity is adjusted to zero and the Kalman filter estimation is triggered when the conditions are met; the calculated nonlinear error increase of the velocity and the position information are corrected; then the velocity information is used as input and a grid cell path integration method is used for further eliminating drift error of the position information.

Description

technical field [0001] The invention relates to an indoor positioning method based on a foot inertial sensor. Background technique [0002] Location-based services (LBS) are very important to people's life. The core technology of LBS is positioning. At present, outdoor positioning technologies include relatively mature GPS positioning and mobile communication system positioning, but indoors often face problems such as wireless signal attenuation and positioning signal decline. In order to meet people's needs for indoor positioning, many indoor positioning technologies have emerged. [0003] Indoor positioning systems are used to provide location information of individuals and devices. Inertial sensor positioning uses the dead reckoning method to achieve continuous and seamless positioning. The dead reckoning method involves three factors: the number of steps, the heading, and the step length. The number of steps and the heading can be obtained through inertial sensors. In...

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Problems solved by technology

However, these methods have some shortcomings: first, after adding sensors such as magnetometers, the magnetic field is easily disturbed, which will directly lead to drift or even errors in the positioning direction; second, WIFI and map positioning methods need to collect data in advance, and the workload Larger, and the positioning accuracy is not high; in addition, the calculation of the particle filter algorithm is very large, it is difficult to meet the requirements of real-time positioning; finally, although the existing similar Kalman method can eliminate the cumulative error, the drift problem cannot be solved

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