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Vector-based complementary filter for attitude estimation: analysis, optimization, and evaluation
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DOI：10.1088/2631-8695/ae2b4f.png)
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Abstract
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Complementary filter (CF) is an efficient data fusion solution for attitude estimation. But the commonly used quaternion-based CF inevitably involves nonlinear and time-consuming calculations that harm its efficiency. Meanwhile, many CF algorithms are based on continuous-time architectures but executed by digital circuits that are actually discrete-time systems, and that may cause performance and even stability issues. To solve these problems, a vector-based CF (VCF) that works under discrete-time condition is introduced in this paper. This VCF takes the measurands of vector sensors (such as accelerometer and magnetometer) as its state variable instead of quaternion, so as to ensure its high computational efficiency. Besides, stability analysis indicates that the VCF can keep stable within a wide range of its only parameter, and thus the optimal parameter setting can be easily found via different approaches provided in this paper. Comparative tests between the proposed VCF and other representative CF algorithms demonstrate that the VCF can achieve desired attitude accuracy with much lower computing time.
Keywords:
Attitude estimation
accelerometer
complementary filter
data fusion
magnetometer
parameter optimization
stability analysis
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1.6
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Cited Papers
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