From Model to Algorithms: Distributed Magnetic Sensor System for Vehicle Tracking
Jiazeng Wang, Junqi Gao, Shuxiang Zhao, Ruichao Zhu, Zekun Jiang, Zhaoqiang Chu, Zhineng Mao, Ying Shen
Abstract
A novel vehicle localization and tracking methods are presented based on magnetic anomaly detection by distributed magnetic sensors. First, taking advantage of total magnetic field, in this article, we propose a total field matching (TFM) method that is immune of rotational vibrations to perform target localization. Instead of directly inverting the nonlinear magnetic dipole equations, we use the TFM approach to find the suboptimal target position, and then apply the linear Kalman filter to tail after the target. Because the relationship is linear between the target dynamics and the localization equations. A case study is performed by simulation to result in an estimated trajectory of ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϕ</i> ) = (70.8 m, 44.9°) that agrees well with the real one of ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϕ</i> ) = (70.5 m, 45°). For a vehicle tracking, the outdoors experiment results show good estimation accuracy based on four different sensor networking configurations.