Millimeter-Scale Magnetic Positioning Using a Single AMR Sensor and BP Neural Network
Guanjun Zhang, Zihe Zhao, Peiwen Luo, Wanli Zhang, Wenxu Zhang
TL;DR
This paper introduces a low-cost, high-precision positioning system using a single magnetic sensor and a neural network for millimeter-scale tracking.
Contribution
The novel use of a single AMR sensor with a BP neural network enables accurate, compact magnetic positioning.
Findings
Simulation RMSEs for X and Z axes are 0.27 mm and 0.26 mm, respectively.
Actual test RMSEs for X, Y, and Z axes are 0.83 mm, 1.15 mm, and 0.85 mm.
Abstract
Unlike conventional positioning systems that rely on multiple sensors, the positioning system proposed in this study uses a single anisotropic magnetoresistive (AMR) sensor to measure the magnetic field of a target permanent magnet. This approach significantly reduces the system hardware cost and complexity, facilitating the miniaturization of positioning systems. Leveraging a BP neural network model, which is shown to be fast and accurate, the positioning system obtains the real-time magnetic field of the target magnet using a single sensor, subsequently converting three-axis magnetic field data into coordinate information to achieve real-time tracking and localization. The results show that the root mean square errors (RMSEs) for the X and Z axes in the simulation are 0.27 mm and 0.26 mm, respectively, while the RMSEs for the X, Y, and Z axes in the actual test are 0.83 mm, 1.15 mm,…
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Taxonomy
TopicsGastrointestinal Bleeding Diagnosis and Treatment · Indoor and Outdoor Localization Technologies · Inertial Sensor and Navigation