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Motion Classi cation and Step Length Estimation for GPS/INS Pedestrian Navigation
KTH, School of Electrical Engineering (EES), Automatic Control.
2012 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The primary source for pedestrian navigation is the well known Global Positioning System. However, for applications including pedestrians walking in urban or indoor environments the GPS is not always reliable since the signal often is corrupted or completely blocked. A solution to this problem is to make a fusion between the GPS and an Inertial Navigation System (INS) that uses sensors attached to the pedestrian for positioning. The sensor platform consists of a tri-axial accelerometer, gyroscope and magnetometer. In this thesis, a dead reckoning approach is proposed for the INS, which means that the travelled distance is obtained by counting steps and multiplying with step length. Three parts of the dead reckoning system are investigated; step detection, motion classification and step length estimation.

A method for step detection is proposed, which is based on peak/valley detection in the vertical acceleration. Each step is then classified based on the motion performed; forward, backward or sideways walk. The classification is made by extracting relevant features from the sensors, such as correlations between sensor signals. Two different classifiers are investigated; the first makes a decision by looking directly on the extracted features using simple logical operations, while the second uses a statistical approach based on a Hidden Markov Model. The step length is modelled as a function of sensor data, and two diffrerent functions are investigated. A method for on-line estimation of the step length function parameters is proposed, enabling the system to learn the pedestrian's step length when the GPS is active.

The proposed algorithms were implemented in Simulink R and evaluated using real data collected from field tests. The results indicated an accuracy of around 2 % of the travelled distance for 8 minutes of walking and running without GPS.


Place, publisher, year, edition, pages
2012. , 60 p.
EES Examensarbete / Master Thesis, XR-EE-RT 2012:011
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-98866OAI: diva2:539673
Educational program
Bachelor of Science in Engineering - Electrical Engineering
Available from: 2012-07-05 Created: 2012-07-04 Last updated: 2012-07-05Bibliographically approved

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