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


  • Yang Tao
  • Xiyin Liang



Kalman filter; data fusion; laser ranging; ultrasonic ranging


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.


Khan S, Tufail M, Khan M T, et al. Real-time recognition of spraying area for UAV sprayers using a deep learning approach[J]. Plos one, 2021, 16(4): e0249436.

Aggarwal S, Kumar N. Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges[J]. Computer Communications, 2020, 149, p270-299.

Chen H, Lan Y, Fritz B K, et al. Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle (UAV)[J]. International Journal of Agricultural and Biological Engineering, 2021, 14(1), p38-49.

Delavarpour N, Koparan C, Nowatzki J, et al. A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges

Kim S, Deshpande V M, Bhattacharya R. Robust Kalman filtering with probabilistic uncertainty in system parameters[J]. IEEE Control Systems Letters, 2020, 5(1), p295-300.

Bao R, Huang L, Andrade J, et al. Review of current methods, applications, and data management for the bioinformatics analysis of whole exome sequencing[J]. Cancer informatics, 2014, 13: CIN. S13779.

Foxlin E. Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter[C]//Proceedings of the IEEE 1996 Virtual Reality Annual International Symposium. IEEE, 1996, p185-194.

Li Yan, Zhang Qi, Wang Teng-jin. Research on Kalman Filter and Multisensor Data Fusion [J]. The Journal of New Industrialization, Vol. 9 (12), p96-100

Hu Changlin,Sun Wei. Multi-sensor Data Fusion Performance Evaluation Indicatiors

and Methods of Computation [J]. Modern Radar, 2013, 35 (03), p41-44+49

Liang Yuming, Xu Lihong, Zhu Bingkun. Ranging Sensors Measurement Adaptive Weighted Fusion On-line [J]. Computer Measurement & Control, 2009, 17 (07), p 1447-1449







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.

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