Because the radio parameter is not considered as the distance measure, but as the feature to train the SVRM model, the side effect of non-line-of-sight is efficiently mitigated.
For the SVR and SVRM model, the radial basis function is used as the kernel and the e-insensitive cost function with [epsilon] = 1 is employed.
The location estimates obtained by the SVR and SVRM are also shown in Figure 1 with C = 200 in (3), [alpha] = 1.5, and [DELTA] = 30 m, where [DELTA] is the distance between the consecutive training locations.
The smoothing results from the Kalman filter and game theory, which take the SVR and SVRM estimates as inputs, are presented in Figure 2.
From Figure 3, it is also shown that smoothing SVRM output by game theory can attain more lower position tracking error than smoothing SVR output by game theory.