A Bayesian framework for calibration and real-time localization of magnetometers using a controllable passive permanent magnet

Magnetic localization systems based on passive permanent magnets (PM) are of great interest due to its ability to provide non-contact sensing and lack of a power requirement of the PM. One sub-problem of particular interest is accurately localizing, in real-time, a single magnetometer with unknown position and orientation, using a passive PM with controllable position and orientation. This is a challenging problem due to the presence of measurement noises and biases, inaccuracy of the magnetic field model, and possible low observability. Bayesian statistical signal processing is a promising approach for this problem, due to its strong mathematical foundation, robustness and suitability for real-time processing. In this work, we develop a Bayesian framework for the individual sensor localization problem which is composed of three parts: magnetic field modeling, sensor calibration, and real-time sensor localization. The effectiveness of the framework is demonstrated using a experimental setup that emulates a possible Transcranial Magnetic Stimulation (TMS) application.

[1]  C. Foster,et al.  Extension of a Non-Linear, Two-Step Calibration Methodology to Include Non-Orthogonal Sensor Axes , 2006 .

[2]  Robert R. Bitmead,et al.  Stochastic observability in network state estimation and control , 2011, Autom..

[3]  Thomas B. Schön,et al.  Calibration of a magnetometer in combination with inertial sensors , 2012, 2012 15th International Conference on Information Fusion.

[4]  J. Jackson,et al.  Classical Electrodynamics, 2nd Edition , 1975 .

[5]  Zhaoying Zhou,et al.  A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package. , 2004, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[6]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[7]  Vesselin P. Jilkov,et al.  A survey of maneuvering target tracking: approximation techniques for nonlinear filtering , 2004, SPIE Defense + Commercial Sensing.

[8]  U-Xuan Tan,et al.  Non-contact parametric estimation and localization of human head for transcranial magnetic stimulation (TMS) , 2013, 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM).

[9]  Alfred O. Hero,et al.  Information Theoretic Approaches to Sensor Management , 2008 .

[10]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[11]  Thomas B. Schön,et al.  MEMS-based inertial navigation based on a magnetic field map , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[12]  Niklas Wahlström.  Target Tracking using Maxwell’s Equations , 2010 .

[13]  Young Soo Suh.  Orientation Estimation Using a Quaternion-Based Indirect Kalman Filter With Adaptive Estimation of External Acceleration , 2010, IEEE Transactions on Instrumentation and Measurement.

[14]  Robert B. McGhee,et al.  An extended Kalman filter for quaternion-based orientation estimation using MARG sensors , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[15]  G.H. Elkaim,et al.  Extension of a two-step calibration methodology to include nonorthogonal sensor axes , 2008, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Shaohui Foong,et al.  Enhanced magnetic localization with artificial neural network field models , 2013, 2013 IEEE International Conference on Robotics and Automation.