Self-adaption Kalman filtering method for autonomous navigation positioning of pedestrians

An adaptive Kalman and autonomous navigation technology, applied in the field of navigation and positioning, can solve the problems of real-time noise changes and real-time deterioration

Active Publication Date: 2015-11-11
BEIJING INFORMATION SCI & TECH UNIV +1
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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, bu

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  • Self-adaption Kalman filtering method for autonomous navigation positioning of pedestrians
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  • Self-adaption Kalman filtering method for autonomous navigation positioning of pedestrians

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[0043] The present invention will be further described below in conjunction with the drawings and specific embodiments:

[0044] figure 1 It is a flowchart of the present invention, in which the IMU module (1-1) for data collection includes an accelerometer, a gyroscope and a magnetometer. The data is solved by strapdown inertial navigation (1-2), and then a suitable observation is selected to obtain a filtering model (1-3), and then the "four conditions" are used to trigger the AKF module (1-4) for filtering. During the normal movement of the human body, the contact time with the ground is milliseconds or longer. At a certain moment, the human body and the ground will reach an instantaneous static state, and when the "four conditions" are met at the same time, it is an instantaneous static state. AKF refers to adaptive Kalman filtering. The AKF module realizes real-time estimation of system noise and statistical characteristics of observed noise, thereby reducing model errors, ...

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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 map informati...

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Application Information

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IPC IPC(8): G01C21/16G01C21/20
CPCG01C21/16G01C21/206
Inventor 高哲李擎苏中付国栋刘宁
Owner BEIJING INFORMATION SCI & TECH UNIV
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