Abstract
The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location. In this paper, we propose the use of weighted multilateration techniques to gain robustness with respect to these inaccuracies, reducing the dependency of having an optimal channel model. In particular, we propose two weighted least squares techniques based on the standard hyperbolic and circular positioning algorithms that specifically consider the accuracies of the different measurements to obtain a better estimation of the position. These techniques are compared to the standard hyperbolic and circular positioning techniques through both numerical simulations and an exhaustive set of real experiments on different types of wireless networks (a wireless sensor network, a WiFi network and a Bluetooth network). The algorithms not only produce better localization results with a very limited overhead in terms of computational cost but also achieve a greater robustness to inaccuracies in channel modeling.
Highlights
The capability of gathering enough data from the environment and the users enables the existence of intelligent spaces, which are able to process the collected information in order to provide useful services or information
Three wireless networks have been used for these experiments: a Wifi network, a Bluetooth network and a wireless sensor network composed of MicaZ motes
In this paper we have proposed the use of two weighted positioning algorithms to calculate the position of a mobile node in an ad hoc network from a set of distance estimations to the anchor nodes
Summary
The capability of gathering enough data from the environment and the users enables the existence of intelligent spaces, which are able to process the collected information in order to provide useful services or information. Our work includes an exhaustive analysis based on both simulated and empirical tests, which shows that the location results are more accurate, as expected for a weighting technique, and more robust to channel estimation errors. With this experimental validation we show that the proposed techniques reduce the localization error with respect to the standard hyperbolic and circular positioning algorithms and that they have a bigger robustness to inaccuracies in the channel estimation.
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