The figures in the following show the mass probability density of the localizzation error, when the bayesian approach is used. This method is described in the pape Savazzi & all; “Radio Imaging by Cooperative Wireless Network: Localizzation Algorithms and Experiments”; IEEE Int. Conf. WCNC 2012.
Scenario C40
In the following table, the Localizzation Error is misured by the bayesian approach by the a-posteriori probability density, based on a gaussian model. The results in the the first row of the table are about the schenario in which we assume that also the cells in the middle in the room, where the sensors are placed, can be crossed. The results in the the second row show the result about the scenario where we assume that the cells can not be crossed.
| Scenario | Parameters | Anchor positions | Error Distribution | Metrics | Notes and video |
|---|---|---|---|---|---|
| C 40 | area: 6.4 X 4.5 [m] anchors: 25 speed: 0.5[step/s] channels: 1 origin: WnLab |  |  | RMSE: 0.867 [m] 75th pc: 0.881 [m] 90th pc: 1.225 [m] | Error distribution in [0.8, 1] m. c40_25sen_1p_1sec.avi |
 | RMSE: 0.77 [m] 75th pc: 0.904 [m] 90th pc: 1.1 [m] | Error distribution in [0.5, 0.75] m. c40_25sen_1p_1sec.avi |
Scenario Pat_2
In the following table, the Localizzation Error is misured by the bayesian approach by the a-posteriori probability density, based on a gaussian model.
| Scenario | Parameters | Anchor positions | Error Distribution | Metrics | Notes and video |
|---|---|---|---|---|---|
| Pat_2 | area: 6.5 X 4.5 [m] anchors: 28 speed: 1[step/s] channels: 1 origin: SPAN Lab |  |  | RMSE: 3.88 [m] 75th pc: 4.700 [m] 90th pc: 5.800 [m] | Error distribution in [1.2, 5.5] m. pat_2_28sen_1p_1sec.avi |
