Assessment of Step Determination in a GPS/Compass/IMU System for Personal Positioning

E. Pulido, H. Kaufmann, R. Quiros:
"Assessment of Step Determination in a GPS/Compass/IMU System for Personal Positioning";
Vortrag: 20th International Technical Meeting of the Satellite Division of the Institute of Navigation - ION GNSS 2007, Fort Worth, Texas; 25.09.2007 - 28.09.2007; in:"Proceedings of ION GNSS 2007", The Institute of Navigation, (2007), S. 1508 - 1515.

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Nowadays, there have been great advances in the location technology, even though the userĀ“s location indoor, outdoor is still a challenge. The personal positioning offers a very interesting field of research because the user walking has an unpredictable behaviour and it is difficult to assume predefined routes or to take into account other implemented location techniques for vehicles or robots.

The combination of GPS with sensors like accelerometers, gyroscopes or magnetometers is often used. The data fusion from these sensors is very important because we have to know the position and orientation constantly.

In this research we are interested in analyzing the system behaviour when the signal GPS is unavailable as when the signal is blocked or in indoor environments. The analysis will be carried out through the assessment of a Dead Reckoning algorithm to improve the position information. The system was tested both indoor and outdoor of the faculty building. The personal positioning system is made up of: a receiver GPS, an electronic compass, and an IMU.

The Dead Reckoning algorithm for pedestrians has two parameters: the travelled distance and the heading. The travelled distance is obtained by means of knowledge of the step length user. The pattern acceleration (forward and vertical) is analysed to determine when a user takes a step; once the step is detected the step length is calculated by a simply neuronal network. All that information is needed to obtain the relative position.

The implemented technique estimated was the Kalman filtering. According to it, the results of the position estimation can be improved if the filtering innovations are evaluated.

We present the bases of an evaluation mechanism to observe divergences and make the corrections to obtain better results.