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The table above shows the positioning accuracy obtained by combining the regression analysis models with and without data shaping.
The table below compares the position accuracy of the triangulation method with that of the proximity method.
In the triangulation method, the parameters are set to n = 2.5, r_{0} = 1 [m], and ес_{0}= -32 [dBm] based on the empirical rule proposed by LaMarca et al.
The estimated position can be represented using this equation.
From these tables there, we can see that the accuracy of the classification model and ensemble learning is low, but the accuracy of the regression analysis is right.
The low accuracy of the classification model and ensemble learning is thought to be due to the inability to make accurate estimates if the data deviates from the teacher data.
The method that combines regression analysis and binary translation data is useful for position estimation because it has a smaller mean error and smaller variance than existing methods. |
IEEE/ICACT20210173 Slide.19
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