Flight state monitoring method for air-ground integrated unmanned aerial vehicle

By performing curve fitting and noise recognition on the drone attitude data, and combining the correlation optimization of filtering amounts, iterative SG filtering is solved, and the problem of filtering or under-filtering in drone attitude data filtering is improved, and the accuracy of flight status monitoring is improved.

CN119714344AActive Publication Date: 2025-03-28SICHUAN XINHANG EXCELLENT INTELLIGENT TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510227787.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-28
Publication Date
2025-03-28
Estimated Expiration
2045-02-28

AI Technical Summary

Technical Problem

When the prior art performs iterative SG filtering on drone attitude data, it may lead to filtering or underfiltering, affecting the accuracy of drone flight status monitoring.

Method used

By performing curve fitting of pitch angle sequences, irregular volatility of data points is determined based on fitting errors, and potential noise is identified in combination with irregular volatility of yaw angle and roll angle. According to the correlation between noise possibility and filtering amount, determine the preferred degree of iteration, and set iteration stop conditions to optimize the filtering process.

Benefits of technology

Effectively distinguish between normal attitude changes and noise, optimize the filtering process, avoid information loss caused by excessive smoothing, improve the accuracy of the final filtering results of the drone attitude data, and improve the accuracy and reliability of flight status monitoring.

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Abstract

The invention belongs to the technical field of data processing, and particularly relates to a flight state monitoring method for an air-ground integrated unmanned aerial vehicle, which comprises the following steps of: performing curve fitting on each pitch angle data point in attitude data of the unmanned aerial vehicle, determining irregular volatility of the pitch angle data points according to a fitting error, and determining a flight state of the unmanned aerial vehicle; according to the difference between the irregular volatility of the pitch angle data point and the irregular volatility of the yaw angle data point and the roll angle data point at the same moment, the noise possibility is determined; performing iterative SG filtering on the pitch angle sequence, determining the optimization degree of each iteration according to the noise possibility and the filtering quantity of each iteration of the pitch angle data points, setting an iteration stop condition according to the optimization degree, and obtaining a final filtering result; and monitoring the flight state of the unmanned aerial vehicle according to the final filtering results of the pitch angle sequence, the yaw angle sequence and the roll angle sequence. The filtering effect of the attitude data can be remarkably improved, and the accuracy of unmanned aerial vehicle attitude monitoring is improved.
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