postuloca:bayesian
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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.
| 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
IIn 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: 7 X 7 [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 |
postuloca/bayesian.1364554007.txt.gz · Last modified: 2013-03-29 10:46 by pietro