An empirical investigation of RSSI-based distance estimation for wireless indoor positioning system

RSSI-based distance estimation techniques for wireless indoor positioning system require extensive offline calibration to construct propagation model in order to describe the relationship between received signal strength and distance. This paper investigates the accuracy of the well-known propagation models against the measured data at indoor building. From the results, the dual slope model exhibits the best propagation model and it is chosen as the reference for further investigation. The accuracy of dual slope model in distance estimation suffers from the degradation due to the presence of Non Line of Sight NLOS condition between mobile station and access point. Therefore, to further improve the accuracy, this paper studies the effect of breakpoint distance and evaluates two simple techniques, running variance and kurtosis index, to identify the NLOS condition. Once the NLOS condition is identified, the best dual slope model can be selected for accurate distance estimation.

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