We are thrilled to share that our latest research paper โEnhancing Anti-interference of Magnetic Tracking: A MagRobustNet-based Framework with Self-supervised Anomaly Detection and Measurements Recoveryโ has been accepted by IEEE Transactions on Industrial Informatics!
Magnetic tracking technology often suffers from diverse and unpredictable interferences in practical applications, such as hard-/soft-iron interferences and sensor saturation, leading to reduced localization accuracy or even tracking failure.
To address these issues, we propose a MagRobustNet-based framework with anomaly detection and measurement recovery. In the first step, disjoint mask sets are used in conjunction with MagRobustNet to detect anomalous measurements subject to disturbances. In the second step, the interfered regions are masked, and MagRobustNet is applied again to recover their expected measurements from neighboring normal data.
Our proposed method not only enhances the tracking systemโs anti-interference capability, but also indicates the interfered regions, offering a new potential diagnostic method for localizing ingested foreign bodies in clinical practice.
Check out our video demonstration at https://lnkd.in/g9TNnsA4
Stay tuned for the paper publication!
Congrats to all co-authors: Shijian Su, Huxin Gao, and Hongliang Ren from the Department of Electronic Engineering, The Chinese University of Hong Kong; Hai Lan and Houde Dai from Quanzhou Institute of Equipment Manufacturing, Haixi Institute, CAS.
