Azimuth Angle Error Compensation Algorithm Based on Maritime Motion Environment

In naval warfare, azimuth angle measurement provides crucial information for vessel avoidance and precise targeting, ensuring navigational safety. However, complex maritime motion conditions, such as ship motion and wave interference, introduce measurement errors. To meet the need for high-precision, intelligent azimuth angle measurement in maritime motion environments, this paper first designs an azimuth angle measurement scheme based on an Inertial Measurement Unit (IMU) and monocular camera. Then, an azimuth angle error compensation algorithm using variational Bayesian (VFVB) adaptive filtering with variable factors is proposed. A position noise covariance model is constructed via inverse gamma distribution, and variable factors are derived from filter innovations to estimate the noise covariance matrix using variational approximation, enhancing IMU attitude accuracy under non-stationary noise. Experimental results show a 25.9% reduction in azimuth angle estimation RMSE, validating the algorithm’s feasibility.

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Publication Details

Published
2024-12-27
DOI
https://doi.org/10.1109/ic2ecs64405.2024.10928420
Primary Topic
Inertial Sensor and Navigation
Type
article
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Azimuth Angle Error Compensation Algorithm Based on Maritime Motion Environment

Wei Gao, Fei Yu, Zixuan Ning, Xin Yin et al.
Inertial Sensor and Navigation
article

Azimuth Angle Error Compensation Algorithm Based on Maritime Motion Environment

Wei Gao, Fei Yu, Zixuan Ning, Xin Yin, Shiwei Fan, Ya Zhang
article en

Abstract

In naval warfare, azimuth angle measurement provides crucial information for vessel avoidance and precise targeting, ensuring navigational safety. However, complex maritime motion conditions, such as ship motion and wave interference, introduce measurement errors. To meet the need for high-precision, intelligent azimuth angle measurement in maritime motion environments, this paper first designs an azimuth angle measurement scheme based on an Inertial Measurement Unit (IMU) and monocular camera. Then, an azimuth angle error compensation algorithm using variational Bayesian (VFVB) adaptive filtering with variable factors is proposed. A position noise covariance model is constructed via inverse gamma distribution, and variable factors are derived from filter innovations to estimate the noise covariance matrix using variational approximation, enhancing IMU attitude accuracy under non-stationary noise. Experimental results show a 25.9% reduction in azimuth angle estimation RMSE, validating the algorithm’s feasibility.

Harbin Institute of Technology (CN)
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Inertial Sensor and Navigation
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