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Vehicle Heading Estimation Using Positioning and Inertial Data-Based Adaptive Tandem Kalman Filter
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DOI：10.1109/TIV.2026.3685424.png)
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Abstract
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This paper proposes an Adaptive Tandem Kalman Filter (ATKF) algorithmic method for accurate, robust, and magnetometer-free heading estimation, based on the inertial and positioning data fusion. It aims to mitigate the heading drift errors, typically occurring in Inertial Measurement Units (IMU), without the use of environmentally sensitive magnetometers. The adaptive nature of the ATKF algorithm allows it to iteratively estimate the input data significance based on the tracked vehicle behavior and perform a corresponding weighted data fusion. A novel tandem structure allows to perform multiple consecutive data processing steps within a single Kalman filter iteration, thus minimizing the delay in algorithm response to the input data. A series of simulated comparison tests with one of the state-of-the-art algorithms have demonstrated the high stability and robustness of the proposed algorithm. It has shown a consistent 40% to 90% improvement in the estimated heading accuracy and precision, depending on the maneuvering intensity and the positioning quality. Simulation results were experimentally validated during the full-scale test campaign. Heading of the highly maneuverable forklift was estimated by the ATKF algorithm with a 1<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> overall median error and 2.3<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> median error during the active movement periods. The proposed method has respectively shown 95% and 93% improvement in initial IMU heading accuracy and precision. Tested magnetometer-based heading estimation methods have also experimentally confirmed their unreliability and inconsistency in industrial applications. The proposed ATKF algorithm may find a variety of applications in the field of robotics and intelligent vehicles and become especially useful in magnetometer-denied environments.
Keywords:
Gyroscope
heading estimation
IMU
kalman filter
magnetometer-free
positioning
sensor fusion
vehicle application
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