Pedestrian integrated navigation method based on multi-error factor tight coupling model

By combining IMU and INS, and utilizing a tightly coupled model of multiple error factors using iterative principal component analysis and Kalman filter, the positioning accuracy problem of pedestrian navigation under complex motion modes was solved, achieving higher navigation accuracy and stability.

CN120651252BActive Publication Date: 2026-07-24BEIJING INST OF TECH
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Patent Information

Application Number
CN202510766497.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-06-10
Publication Date
2026-07-24
Estimated Expiration
2045-06-10

AI Technical Summary

Technical Problem

Existing upper limb strapdown pedestrian dead reckoning methods suffer from problems such as inaccurate estimation results of statistical step length models and deviations between heading and pedestrian movement orientation under complex motion patterns.

Method used

By employing a multi-error-factor tightly coupled model (MetCM) combined with an inertial measurement unit (IMU) and an inertial navigation system (INS), and using iterative principal component analysis and Kalman filters, the angular error between the heading and the direction of travel is compensated, thereby improving positioning accuracy.

Benefits of technology

The positioning accuracy of pedestrian navigation was significantly improved under complex motion modes, the computational load was reduced, and the influence of noise was reduced by iterative principal component analysis, thereby improving the stability and accuracy of the positioning system.

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Abstract

The present disclosure provides a pedestrian integrated navigation method based on a multi-error factor tight coupling model, which uses a sensor arranged on the chest to collect inertial measurement data; an INS performs inertial navigation calculation based on the inertial measurement data, and updates the INS position; an iterative principal component analysis method is used to determine the angle error between the heading and the walking direction under multi-gait conditions, to compensate for the MetCM heading based on quaternion calculation, and to obtain the updated and compensated MetCM position; a Kalman filter combining the MetCM model and the INS error model is constructed to estimate the position error, to compensate for the INS position and the MetCM position, and to output the calibrated position. The present disclosure can effectively improve the positioning accuracy of autonomous navigation.
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