Weighted Least Squares Techniques for Improved Received Signal Strength Based Localization

Sensors

Paula Tarrío¹  Ana M. Bernardos² 

[1] Data Processing and Simulation Group, Universidad Politécnica de Madrid, ETSI. Telecomunicación, Avda. Complutense 30, 28040 Madrid, Spain;

关键词: localization;    positioning;    wireless networks;    least squares;    received signal strength;    channel model estimation;   

DOI  :  10.3390/s110908569

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.