ADAPTIVE FILTERING ALGORITHMS ENHANCE THE ACCURACY OF LOW-COST INERTIAL/MAGNETIC SENSING IN PEDESTRIAN NAVIGATION SYSTEMS
Abstract
The availability of inexpensive, miniaturized sensors and the development of efficient and accurate filtering algorithms are important elements in order that pedestrian navigation system (PNS) will become an enabling technology for monitoring unconstrained daily living activities of human subjects.
In this paper, we develop and test a computational method that estimates the path traveled by a walker from in-shoe mounted tri-axis inertial and magnetic sensors. The interest for inertial and magnetic sensors stems from their low cost, suitable form factors, small power consumption, which may give rise to self-contained, portable PNS whose hindrance to walking is limited.
An important element in our approach is represented by an extended Kalman filter (EKF), whose aim is to estimate the body part orientation and to perform the in-line calibration of the tri-axis magnetometer. Two validation tests are applied to either acceleration or magnetic vector measurements, in order to adapt the measurement noise covariance matrix against the effects of body motion and external magnetic fields on the sensed gravity and earth's magnetic fields involved in orientation determination. Additionally, some biomechanical facts about how humans normally move are exploited to enhance the positioning performance of the developed method.
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