Received signal strength based device-free localization applications utilize a model that relates the measurements to position of the wireless sensors and person, and the underlying inverse problem is solved either using an imaging method or a nonlinear Bayesian filter. In this paper, it is shown that Bayesian filters nearly reach the posterior Cramer-Rao bound and they are superior with respect to imaging approaches in terms of localization accuracy because the measurements are directly related to position of the person. However, Bayesian filters are known to suffer from divergence issues and in this paper, the problem is addressed by introducing a novel Bayesian filter. The developed filter augments the measurement model of a Bayesian filter with position estimates from an imaging approach. This bounds the filter's measurement residuals by the position errors of the imaging approach and as an outcome, the developed filter has robustness of an imaging method and tracking accuracy of a Bayesian filter. The filter is demonstrated to achieve a localization error of 0.11 m in a 75 m2 open indoor deployment and an error of 0.29 m in a 82 m2 apartment experiment, decreasing the localization error by 30-48% with respect to a state-of-the-art imaging method.

中文翻译：

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一种用于基于 RSS 的无设备定位和跟踪的新型贝叶斯滤波器

基于接收信号强度的无设备定位应用程序利用将测量值与无线传感器和人的位置相关联的模型，并且使用成像方法或非线性贝叶斯滤波器解决潜在的逆问题。在本文中，表明贝叶斯滤波器几乎达到了后 Cramer-Rao 界限，并且它们在定位精度方面优于成像方法，因为测量与人的位置直接相关。然而，众所周知，贝叶斯滤波器存在发散问题，在本文中，该问题通过引入一种新的贝叶斯滤波器来解决。开发的滤波器通过成像方法的位置估计增强了贝叶斯滤波器的测量模型。这限制了过滤器' s 测量残差通过成像方法的位置误差，作为结果，开发的滤波器具有成像方法的鲁棒性和贝叶斯滤波器的跟踪精度。该滤波器在 75 平方米的开放式室内部署中实现了 0.11 m 的定位误差，在 82 平方米的公寓实验中实现了 0.29 m 的定位误差，相对于当前状态将定位误差降低了 30-48%。 -艺术成像方法。