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The figures in the following show the mass probability density of the localizzation error, committed through the bayesian approach described in the paper Savazzi & all; “Radio Imaging by Cooperative Wireless Network: Localizzation Algorithms and Experiments”; IEEE Int. Conf. WCNC 2012.
Scenario C40
The figures below show the results of the scenario represented in the figure 'Scenario C40', where the surface localization has dimensions 6.4 m X 4.5 m, the sensors used to collect the measures are 25, the target move along a path at the rate of one step every two seconds and the RSSI values are collected on a single radio channel. The calibration Phase required by the algorithm has been performed on 22 cells.
This figure shows the localizzation error distribution when the only image radio tomography information is taken into account.
This figure shows the localizzation error distribution when the image radio tomography information is filtered out by the Kalman's Filter.
Through the results shown into the TAB. 1 arises that the Kalman filter does not give significant advantages, in fact, the error in both cases, “with and without” Kalman, is distributed within the interval [0.5, 1.75] meters.
The figures below show the results of the scenario represented in the figure 'Scenario Pat_1', where the surface localization has dimensions 8.4 m X 6.5 m, the sensors used to collect the measures are 33, the target move along a path at the rate of one step every two seconds and the RSSI values are collected on five radio channels, but only the two channels with the best signal strength are taken into account. The data for this scenario are granted by Dr. Patwari & all.