A Multi-Layer Position-Pose Fusion Framework for Joint Magnetoquasistatic Field and IMU Positioning

Magnetoquasistatic (MQS) field positioning has demonstrated significant potential for emergency rescue applications due to its strong penetration and non-reliance on pre-deployment. However, its accuracy is notably impaired by metal interference and distance attenuation. Inertial Measurement Units (IMUs) can reliably provide motion data even in environments affected by metal and electromagnetic interference, but they suffer from cumulative drift over time. Effectively, combining MQS field and IMU positioning to harness their respective advantages presents a crucial challenge. To address this, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-Layer Position-Pose Fusion</i> (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MP2F</i>) framework that integrates MQS field with IMU data to enhance position and pose estimation. The MP2F framework comprises three layers: a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quaternion-based Pose Fusion Layer</i> (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QPFL</i>), a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Kalman Filter-based Position Fusion Layer</i> (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KFFL</i>), and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Global Position-Pose Fusion Layer</i> (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GP2FL</i>). Specifically, QPFL utilizes the Extended Kalman Filter (EKF) to effectively mitigate magnetic field distortion and IMU drift, thereby significantly enhancing pose estimation precision. Next, KFFL incorporates the fused pose estimation from QPFL into an inertial navigation motion model, and leverages MQS field observations to further improve positional accuracy. Finally, GP2FL formulates a nonlinear least squares optimization problem by marginalizing prior factors, inertial sensor factors, and Kalman fusion outputs, enabling globally optimized state estimation. Comprehensive simulation results and analyses prove that the proposed MP2F framework achieves high-precision position and pose estimation in complex emergency scenarios, with strong robustness. Experimental results in real-world environments show that the proposed MP2F achieves improvements in positioning accuracy of 61.1%, 58.7%, 48.4%, and 50.2% over EKF, iMag+, GWO-PF, and MagLoc, respectively.