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The Cartesian coordinates provided by the localization system are fed to the proposed observer in order to estimate the orientation of the vehicle.
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By this way, the localization system minimizes the number of RTS/CTS frames exchange and hence the use of the IEEE 802.11 channel which could slow down the packet delivery on the wireless network.
Building on this finding, the robustness of the localization system is enhanced by modulating the importance of features according to the entropy measure.
The accuracy of the localization results and the availability of the localization system are influenced by several factors, such as the characteristics of the devices used, the surrounding environment, and the distance from reference stations.
Moreover, the parameters that characterize can be obtained previously to the localization process by the estimates performed in the environment where the localization system is going to be deployed.
The practical experiment was implemented based on the localization system for indoor environment proposed by BluePass [16].
An ultra-low-power and dedicated hardware implementation of the localization system is presented.
Figure 2 Architecture of the localization system.
Figure 2 Illustration of the localization estimation problem and the desired output from the localization system.
The distance estimates errors are the largest contributors to overall error of the localization system, and only a small fraction of this error is generated by position computation and localization algorithm [4, 24].
It can be adopted by any localization system as long as they use the RSS as measurement, such as WLAN, Zigbee, Bluetooth, and GSM-based systems.
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