Weighted least squares techniques for improved received signal strength based localization.
Weighted least squares techniques for improved received signal strength based localization.
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DOI：
10.3390/s110908569
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Casar JR
中科院分区：
文献类型：
--
作者：
Tarrío P;Bernardos AM;Casar JR
无线定位系统的实际部署需要最大限度地减少校准程序，同时提高位置估计精度。使用传播信道模型的接收信号强度定位技术是最简单的替代方法，但它们通常是在假设无线电传播模型被完美地先验表征的情况下设计的。在实践中，这一假设并不成立，定位结果受到用于计算位置的理论、粗略校准或不完善的信道模型的不准确性的影响。在本文中，我们建议使用加权多倍率技术来获得关于这些不准确性的鲁棒性，减少对拥有最优信道模型的依赖。特别是，我们提出了两种基于标准双曲和圆形定位算法的加权最小二乘技术，它们特别考虑了不同测量的精度，以获得更好的位置估计。通过数值模拟和在不同类型的无线网络（无线传感器网络、WiFi网络和蓝牙网络）上的一组详尽的真实实验，将这些技术与标准的双曲和圆形定位技术进行了比较。该算法不仅在计算成本方面产生了更好的定位结果，而且对信道建模中的不准确性具有更强的鲁棒性。
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.
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