An Adaptive Kalman Filter Method for Pedestrian Autonomous Navigation and Positioning

Abstract

The invention discloses a self-adaption Kalman filtering method for autonomous navigation positioning of pedestrians. The method comprises the following steps: connecting an MEMS-IMU system integrating an accelerometer, a gyroscope and a magnetometer to a human body, and carrying out IMU data acquisition during movement of pedestrians; and establishing a self-adaption filtering model containing eighteen-dimensional state variables and nine-dimensional observed quantity, and carrying out recursive filtering while meeting four conditions, wherein a time varying noise statistical estimator is used for estimating and correcting system noise and observing the statistical character of noise in real time. According to the invention, on the basis of using zero-speed correction as error compensation correcting algorithm, a self-adaption filtering method fusing human body moving character is designed, noise interference signals caused by shake of the human body can be processed in real time, and the precision of autonomous navigation positioning of the pedestrians is effectively increased. The method disclosed by the invention is strong in stability and good in real-time property, and no extra hardware cost is increased.

Description

technical field [0001] The invention belongs to the technical field of navigation and positioning, and in particular relates to an adaptive Kalman filter method for autonomous navigation and positioning of pedestrians. Background technique [0002] In recent years, navigation and positioning technology is still developing rapidly, and most of them are satellite-based navigation systems, and are only suitable for outdoor environments, such as GPS, which is the most widely used, in occluded environments such as urban buildings, mountains, forests and underground buildings. , its signal is weak and its usability is poor. [0003] With the acceleration of people's life rhythm, the demand for autonomous navigation and positioning of pedestrians is becoming more and more urgent. Especially in the indoor environment, such as rescue in emergency environments such as fires and building collapses, and the need to find people or places in shopping malls. Many people use the method of...

AI Technical Summary

Problems solved by technology

This method is to linearize the system locally, which is more in line with the actual situation of pedestrian movement than the classical Kalman filter method, so the positioning accuracy has been improved, but it still does not solve the problem of the influence of real-time noise changes, and each update must It can only be completed by calculating the Jacobian matrix, and the real-time performance becomes worse

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