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A First-Order Differential Data Processing Method for Accuracy Improvement of Complementary Filtering in Micro-UAV

Xudong Wen1, Chunwu Liu2, Zhiping Huang3

  • 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China. wenxudong13@163.com.

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Summary
This summary is machine-generated.

This study introduces a new differential-nonlinear complementary filtering (D-NCF) algorithm to improve attitude estimation accuracy for micro-unmanned aerial vehicles (UAVs). The D-NCF algorithm significantly reduces errors in roll, pitch, and yaw angles compared to traditional methods.

Keywords:
attitude estimationdata processingmicro-UAVnonlinear complementary filtering (NCF)sensor fusion

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Area of Science:

  • Robotics and Control Systems
  • Sensor Fusion and Data Processing
  • Aerospace Engineering

Background:

  • Complementary filtering algorithms offer low computational complexity and real-time performance, making them suitable for attitude estimation in micro-unmanned aerial vehicles (UAVs) with inertial measurement units (IMUs).
  • However, the low attitude estimation accuracy of traditional complementary filtering methods limits their practical applications.
  • Existing research has focused on improving accuracy through algorithm modifications, with limited attention to data processing techniques.

Purpose of the Study:

  • To enhance the attitude estimation accuracy of complementary filtering algorithms by improving gyroscope data processing.
  • To introduce a novel differential-nonlinear complementary filtering (D-NCF) algorithm that integrates improved data processing with existing nonlinear filtering techniques.
  • To validate the effectiveness of the proposed D-NCF algorithm through experimental comparison with the basic nonlinear complementary filtering (NCF) algorithm.

Main Methods:

  • A real-time first-order differential data processing algorithm was developed for gyroscope data, incorporating an adaptive parameter adjustment strategy.
  • The proposed differential-nonlinear complementary filtering (D-NCF) algorithm was created by combining the enhanced data processing method with the standard nonlinear complementary filtering (NCF) algorithm.
  • Experimental validation was performed to compare the attitude estimation accuracy of the D-NCF algorithm against the NCF algorithm.

Main Results:

  • The first-order differential data processing algorithm effectively corrects gyroscope data, leading to improved attitude estimation.
  • The D-NCF algorithm demonstrated a significant reduction in Root Mean Square Error (RMSE) for all attitude angles compared to the NCF algorithm.
  • Specifically, RMSE for roll angle decreased from 1.1653 to 0.5093, pitch angle from 2.9638 to 1.5542, and yaw angle from 0.9398 to 0.6827.

Conclusions:

  • The proposed first-order differential data processing algorithm enhances gyroscope data quality for attitude estimation.
  • The differential-nonlinear complementary filtering (D-NCF) algorithm offers superior attitude estimation accuracy compared to the basic nonlinear complementary filtering (NCF) algorithm for micro-UAVs.
  • The D-NCF algorithm presents a viable solution for improving the precision of attitude estimation in low-cost IMU-equipped micro-UAVs.