Abstract
By using the principle of sensor data consistency and combining the kalman filter algorithm with the bayesian algorithm, the accuracy of the algorithm is significantly improved under the condition of large data volume and complex system. An adaptive multi-sensor data consistency algorithm based on node credibility proposes the internal correlation between the sensor credibility and the data values it provides, and the final data fusion results are obtained by fusion of the optimized data through the sensor credibility. The advantages of this algorithm include: (1) the data is optimized by kalman filter, which reduces the influence of the data provided by the sensor with low reliability on the fusion result; (2) bayesian algorithm is adopted to calculate the correct probability of data values and the reliability of the sensor, which overcomes the inherent uncertainty of the sensor and improves the accuracy of data fusion. The results show that compared with the traditional data consistency algorithm, this algorithm can further improve the accuracy of fusion results and reduce the position error.
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Acknowledgement
This work is partly supported by The Project Supported by Science and Technology Project in Shaanxi Province of China (Program No.2019ZDLGY07-08). The International Science and Technology Cooperation Program of the Science and Technology Department of Shaanxi Province, China (Grant No.2018KW-049), and the National Natural Science Foundation of China (61702414), Natural Science Basic Research Program of Shaanxi (2018JQ6078).
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Chen, Y., Ma, X., Xia, H., Wang, Z., Yv, Z. (2020). An Adaptive Multi-sensor Data Consistency Algorithm Based on Node Credibility. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_108
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DOI: https://doi.org/10.1007/978-3-030-32591-6_108
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