A Wearable Pedestrian Localization and Gait Identification System Using Kalman Filtered Inertial Dat

Abstract: In pedestrian inertial navigation, multi-sensor fusion is often used to obtain accurate
heading estimates. As a widely distributed signal source, the geomagnetic field is convenient to
provide sufficiently accurate heading angles. Unfortunately, there is a broad presence of artificial
magnetic perturbations in indoor environments, leading to difficulties in geomagnetic correction.
In this paper, by analyzing the spatial distribution model of the magnetic interference field on the
geomagnetic field, two quantitative features have been found to be crucial in distinguishing normal
magnetic data from anomalies. By leveraging these two features and the classification and regression
tree (CART) algorithm, we trained a decision tree that is capable of extracting magnetic data from
distorted measurements. Furthermore, this well-trained decision tree can be used as a reject gate in a
Kalman filter. By combining the decision tree and Kalman filter, a high-precision indoor pedestrian
navigation system based on a magnetically assisted inertial system is proposed. This system is then
validated in a real indoor environment, and the results show that our system delivers state-of-the-art
positioning performance. Compared to other baseline algorithms, an improvement of over 70% in the
positioning accuracy is achieved.