MEMS pedestrian navigation method based on ZIHR heading angle correction algorithm

Abstract

The invention provides an MEMS pedestrian navigation method based on ZIHR heading angle correction algorithm. The method includes: 1. conducting initial alignment on an MEMS pedestrian navigation system with an accelerometer and a magnetometer at a static moment; 2. working out an inertial navigation calculation equation and an error equation of the MEMS pedestrian navigation system; 3. conductingzero velocity state detection with the output values of a gyroscope and the accelerometer; 4. working out the relationship between the heading angle difference of adjacent moments under a zero velocity state, gyroscopic drift and a heading error angle in ZIHR (zero integrated heading rate) correction algorithm; and 5. establishing a simplified MEMS pedestrian navigation UKF filter model, and performing UKF filtering. The method provided by the invention maximumly utilizes the information of static moment, has uncomplicated calculation amount, and can well inhibit the divergence of the headingerror angle. Use of the UKF filter for real-time feedback correction at a zero velocity moment can well inhibit the problem of navigation parameter error divergence after long-time operation of a low-precision MEMS sensor, and improve the positioning precision of a pedestrian navigation system.

Description

technical field [0001] The invention relates to a pedestrian navigation method, in particular to a pedestrian navigation method of inertial navigation technology. Background technique [0002] Pedestrian navigation system is an important branch in the field of navigation and positioning technology, which can provide reliable safety guarantee for high-risk workers engaged in fire fighting and emergency rescue. The core of traditional pedestrian navigation is GPS, but it is difficult to play a role when GPS is located in urban high-rise buildings and dense vegetation. The pedestrian positioning system with MEMS inertial sensor as the core does not need to consider the above problems. Because MEMS inertial sensors have the advantages of small size, low cost, light weight, etc., the research on pedestrian navigation with MEMS sensors has become a hot spot. Non-GPS people tracking systems typically use gyroscopes to estimate the user's heading change. Gyroscopes measure the ra...

AI Technical Summary

Problems solved by technology

[0010] When using the Kalman filter for zero-speed correction, although the speed error and horizontal attitude angle error can be suppressed, the heading error angle still cannot be well corrected

Through the analysis of the observability of the Kalman filter equation, it is found that the observability of the heading error angle is very poor, and the Kalman filter cannot accurately estimate the state quantity with poor observability

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