Research on adaptive data fusion algorithm for point and surface ranging sensor

Authors

  • Yang Tao
  • Xiyin Liang

DOI:

https://doi.org/10.54097/03bjsb42

Keywords:

Kalman filter; data fusion; laser ranging; ultrasonic ranging

Abstract

When the UAV is close to the ground target, the measurement of distance has the needs of real-time, wide range and high stability. A single type of sensor cannot meet the requirements of measurement range and measurement angle at the same time. In this paper, through the construction of multi-sensor data acquisition experimental system, the actual performance of improved Kalman filter and adaptive weighting data fusion algorithms applied to point ranging and surface ranging sensor data fusion is studied, and the optimization algorithm and results for laser sensor and ultrasonic sensor fusion ranging are obtained. The Kalman filter algorithm only weights the measurement data at the current sampling time, and the weighting coefficients of each sensor are adjusted in an adaptive manner through the online estimation of the variance of the sensor 's measurement data, so that the mean square error of the fusion result is always minimized, and the two sensors are functionally complementary. The experimental results show that this method improves the accuracy, stability and smoothness of the cooperative ranging between the point ranging sensor and the surface ranging sensor, and can better meet the real-time ranging needs of the UAV when approaching the ground target.

References

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Published

31-12-2023

Issue

Section

Articles

How to Cite

Tao, Y., & Liang, X. (2023). Research on adaptive data fusion algorithm for point and surface ranging sensor. Computer Life, 11(3), 16-20. https://doi.org/10.54097/03bjsb42

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