Step Detection for ZUPT-Aided Inertial Pedestrian Navigation System Using Foot-Mounted Permanent Magnet
| Autor: | Zakaria Kasmi, Jörg Blankenbach, Abdelmoumen Norrdine |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Engineering
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Inertial reference unit ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology Accelerometer 01 natural sciences law.invention Computer Science::Robotics Hardware_GENERAL Inertial measurement unit law Control theory Dead reckoning 0202 electrical engineering electronic engineering information engineering ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS Electrical and Electronic Engineering Instrumentation Inertial navigation system business.industry 010401 analytical chemistry 020206 networking & telecommunications Gyroscope Kalman filter 0104 chemical sciences Step detection business |
| Zdroj: | IEEE Sensors Journal. 16:6766-6773 |
| ISSN: | 2379-9153 1530-437X |
| Popis: | A foot-mounted pedestrian dead reckoning system is a self-contained technique for indoor localization. An inertial pedestrian navigation system includes wearable MEMS inertial sensors, such as an accelerometer, gyroscope, or digital compass, which enable the measurement of the step length and the heading direction. Therefore, the use of zero velocity updates is necessary to minimize the inertial drift accumulation of the sensors. The aim of this paper is to develop a foot-mounted pedestrian dead reckoning system based on an inertial measurement unit and a permanent magnet. Our approach enables the stance phase and the step duration detection based on the measurements of the permanent magnet field during each gait cycle. The proposed system involves several parts: inertial state estimation, stance phase detection, altitude measurement, and error state Kalman Filter with zero velocity update and altitude measurement update. Real indoor experiments demonstrate that the proposed algorithm is capable of estimating the trajectory accurately with low estimation error. |
| Databáze: | OpenAIRE |
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