IMMNN: Robust Wireless Electromagnetic-Inertial Fusion Tracking via Learning An Adaptive IMM

Abstract

Wireless Electromagnetic Tracking (WEMT) enables non-line-of-sight (NLoS) pose estimation in robotics but faces accuracy limitations from restricted operational range and environmental interference. This paper proposes a WEMT-inertial fusion system enhanced by a learning-based Interacting Multiple Model (IMMNN) to address these challenges. The framework integrates a multi-transmitter array with a WEMT-IMU fusion tracker, leveraging IMMNN to mitigate performance degradation caused by nonlinear spatial noise and motion uncertainty in dynamic, array-based environments. IMMNN employs a graph attention network to dynamically model spatial correlations among array units, adaptively optimizing state transition probabilities across motion models. A gated recurrent framework further enhances robustness by analyzing residual sequences to suppress transient noise and outliers. Experimental results demonstrate that the proposed system achieves a root-mean-square error (RMSE) of 30.4 mm over an expanded 1.9×1.9 m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> operational area. The graph attention mechanism enables adaptive spatial noise suppression and ensures stable tracking under rapid motion and electromagnetic disturbances. By synergizing model-driven filtering with data-driven learning, IMMNN effectively improves accuracy and robustness, advancing high-precision WEMT solutions for complex robotic applications.