Open AccessImproved pedestrian tracking through Kalman covariance error selective reset
Author(s) -
Rubia E.,
DiazEstrella A.
Publication year - 2013
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2013.0213
Subject(s) - reset (finance) , kalman filter , covariance intersection , covariance matrix , covariance , position (finance) , computer science , tracking (education) , inertial measurement unit , control theory (sociology) , inertial navigation system , algorithm , mathematics , estimation of covariance matrices , statistics , artificial intelligence , orientation (vector space) , control (management) , psychology , pedagogy , financial economics , economics , geometry , finance
Kalman filtering is one of the most widely used approaches to handling inertial sensors in pedestrian tracking systems. This technique uses a covariance error matrix to estimate position. This reported study leads to the hypothesis that there is no correlation between some elements of this matrix from one step to the next. Therefore, a selective reset of these elements at the end of each step improves position estimation. A set of these elements is proposed, and a statistical study is conducted using 32 data traces from the same path. Four parameters are analysed: the correction mean length, the position error, the altitude error and the travelled distance. As a result, all of these parameters obtain a loose statistical significance when the covariance error selective reset is applied.