A Sequential Adaptive Linear Kalman Filter Based on the Geophysical Field for Robust MARG Attitude Estimation
Source abstract
Abstract
In magnetometer, accelerometer, and rate gyroscope (MARG) attitude and heading reference systems, accelerometers and magnetometers are susceptible to external acceleration and soft/hard magnetic anomalies, which reduce the attitude estimation accuracy. To address this problem, a sequential adaptive Kalman filter algorithm based on the geophysical field is proposed for anti-interference MARG attitude estimation. By establishing the linear system model based on the gravitational field and geomagnetic field, the singularity and coupling in other system models are avoided. Additionally, the sequential Sage–Husa adaptive strategy is employed to estimate the measurement noise parameters in real time by a specific force and magnetic vector, which suppresses the impact of external acceleration and the soft/hard magnetic anomalies. To verify the effectiveness and advancement of the proposed algorithm, a series of anti-interference experiments were designed. Experimental results show that, compared with the geophysical-field-based Kalman filter algorithm without an adaptive strategy, the proposed improved algorithm reduces the yaw maximum error by over 94% and inclination maximum error by over 21%, which improves the MARG attitude estimation robustness and makes this algorithm superior to the existing three adaptive strategies and two algorithms.
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Research context
OpenAlex topics: Inertial Sensor and Navigation · Target Tracking and Data Fusion in Sensor Networks · Indoor and Outdoor Localization Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Science Island Branch, Graduate School of USTC, Hefei 230026, China
Funding
National Key Research and Development Program of China
References (40)
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