A marine buoy multi-source fusion positioning method and system based on factor graph optimization

By using a multi-source fusion positioning method with factor graph optimization and adaptive weighting of marine environmental data, the problems of multipath interference and inertial error of marine buoys under dynamic sea conditions were solved, and high-precision and continuous marine buoy positioning was achieved.

CN122260372BActive Publication Date: 2026-07-24FIRST INSTITUTE OF OCEANOGRAPHY MNR
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
FIRST INSTITUTE OF OCEANOGRAPHY MNR
Filing Date
2026-05-25
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing marine buoy positioning systems suffer from reduced positioning accuracy and reliability due to satellite signal multipath interference caused by wave reflection and inertial calculation errors under dynamic sea conditions.

Method used

A multi-source fusion positioning method based on factor graph optimization is adopted. By constructing factor graph state nodes and multi-source factors, adaptive weighting and incremental optimization are performed in combination with marine environmental data. Ocean current and wave constraint factors are used to suppress multipath interference. When GNSS signal is interrupted, IMU pre-integration and ocean dynamic constraints are used to provide auxiliary positioning. Multiple buoys are coordinated for correction and anomaly detection.

Benefits of technology

It effectively suppressed multipath interference on the sea surface, improved the reliability of satellite observation data and the accuracy of multi-source fusion positioning, maintained the continuity and real-time nature of positioning services, and met the high-precision positioning requirements of buoys under dynamic sea conditions.

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Abstract

The application discloses a marine buoy multi-source fusion positioning method and system based on a factor graph optimization, which acquires GNSS observation data, IMU measurement data and marine environment auxiliary data; a factor graph containing state nodes, GNSS position factors, IMU pre-integration factors and marine dynamics constraint factors is constructed, and wave and current theories are used to constrain buoy movement; in view of multipath effects, marine surface reflection geometry and marine root mean square wave height are combined to calculate a multipath weighting factor, and a GNSS covariance matrix is adaptively adjusted; according to IMU data, a sea state level is discriminated, and an edge window length and a trigger interval of incremental smoothing solving are adaptively linked and adjusted; through adjacent buoy ranging information, collaborative constraints are constructed, and based on Mahalanobis distance and chi-square distribution threshold value detection, abnormalities are detected and local reconstruction is performed. The application effectively suppresses marine surface multipath interference, slows down the accumulation of calculation errors during signal interruption, and realizes high-availability continuous positioning under limited computing power.
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