A robust kalman filtering method

By employing a robust Kalman filter method, anomalies in observations are detected and handled. The scaling factor and covariance are used for updates, which solves the pose failure problem of the Kalman filter under outliers and achieves higher accuracy and stable estimation performance.

CN116361616BActive Publication Date: 2025-10-24VALLEY OF SCI & TECH OF CHINA
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Patent Information

Application Number
CN202310344333.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Priority Date
2022-12-16
Filing Date
2023-03-31
Publication Date
2025-10-24
Estimated Expiration
2043-03-31

AI Technical Summary

Technical Problem

Existing Kalman filters are prone to pose failure when faced with anomalies in observations, and traditional robust filtering methods are inefficient and cannot effectively handle outliers in all cases.

Method used

A robust Kalman filter method is adopted. By setting a preset number of sampling times, outliers are detected and the scaling factor and covariance are updated when an anomaly is detected. The robust Kalman filter algorithm is used to process the observations, and the standard Kalman filter algorithm is used for recursive updates to ensure the continuity and stability of the method.

Benefits of technology

It improves the computational accuracy and robustness of Kalman filtering, effectively resists the influence of outliers, expands the application range, and ensures stable estimation performance under various conditions.

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Abstract

The application relates to a robust Kalman filtering method, which comprises the following steps: setting a preset sampling number N, initializing filtering, defining the initial value of a state vector and a covariance matrix; updating an observation vector and calculating a criterion index; detecting whether an abnormal value exists; if the abnormal value is detected, updating parameters by using a robust Kalman filtering algorithm, detecting again whether the abnormal value exists after the parameters are updated; if no abnormal value exists, recursively updating by using a standard Kalman filtering algorithm; repeating the sampling, and ending the program when the sampling number reaches the preset sampling number. The above scheme provided by the application can update parameters by using the robust Kalman filtering algorithm when the abnormal value is detected, so that the efficiency and application range are improved.
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