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Solution for heterogeneous data fusion based on autonomous classification
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DOI：10.1016/j.measurement.2024.116326.png)
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摘要
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Variance component estimation (VCE) is an essential component of heterogeneous data fusion, however, VCE has problems with stability and reliability in random models. To progress data fusion research, this paper proposes a new solution that establishes a data fusion model based on the theory of Msplit estimation. To determine the classification number of heterogeneous data accurately, a median function was applied for autonomous classification using Msplit estimation, according to the basic characteristics of random error distribution, a method of autonomous classification is established for heterogeneous data. Especially, the fusion method presented in this paper does not rely on prior information of observation data and can effectively overcome the limitations of current methods for data fusion based on the theory of VCE. An example of coordinate affine transformation was used to verify that the model proposed in this paper could achieve effective fusion of heterogeneous data.
Keyword:
Autonomous classification
Heterogeneous data fusion
M split estimation
Median function
期刊
IF:
5.6
论文数:
2.0W
被引数:
5.4W
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