Multi-Magnet Tracking Method using Extended Kalman Filter

Ge, Han1; Song, Shuang1; Wang, Jiaole1; Meng, Max Q-H2

10.1109/SENSORS47087.2021.9639620

2021

20th IEEE Sensors Conference

1930-0395

978-1-7281-9502-5

2021 IEEE SENSORS

2021-October

1-4

OCT 31-NOV 04, 2021

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Permanent magnet-based localization can provide a wireless and low-cost solution for tracking surgical instruments in Minimally Invasive Surgery (MIS). To meet the requirement of simultaneously tracking of multiple surgical instruments in clinical application scenarios, we propose a probabilistic filtering approach to realize multi-magnet positioning. The tracking system includes a magnetic sensor array and magnetic targets. Random walk model has been used to formulate the system equation, and the measurement equation is established with the magnetic dipole model. Based on the Extended Kalman Filter (EKF) algorithm, the nonlinear problem can be solved by estimating the positions and orientations of multiple magnets. The feasibility of the proposed method is verified by both static and dynamic experiments. The results show that the filtering-based multi-target magnetic tracking algorithm enables low-latency tracking with reasonable accuracy.

multiple object tracking magnetic tracking extended Kalman filter

其他

英语

CPCI-S ; EI

National Natural Science Foundation of China[61803123]

Engineering ; Remote Sensing

Engineering, Electrical & Electronic ; Remote Sensing

WOS:000755468300132

20220511559048

Magnetism ; Permanent magnets ; Robotic surgery ; Surgical equipment ; Target tracking

Medicine and Pharmacology:461.6 ; Biomedical Equipment, General:462.1 ; Magnetism: Basic Concepts and Phenomena:701.2 ; Electric Components:704.1 ; Robot Applications:731.6

Web of Science

1.Harbin Inst Technol, Sch Mech Engn & Automat, Shenzhen, Peoples R China
2.Southern Univ Sci & Technol, Shenzhen, Peoples R China

Ge, Han,Song, Shuang,Wang, Jiaole,et al. Multi-Magnet Tracking Method using Extended Kalman Filter[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:1-4.