基于贝叶斯背景模型的免携带设备目标定位算法

Bayesian Rule-based Background Model for Object Device-free Localization

免携带设备目标定位不需要目标携带任何电子设备或标签来对人或其他物体进行定位。针对现有射频层析成像算法在多径环境中定位精度不理想的问题,提出了一种基于贝叶斯背景模型的定位算法。该算法首先将斜拉普拉斯分布和贝叶斯理论相结合来建立贝叶斯背景模型,用于排除冗余链路;然后对接收信号强度的变化值进行加权处理,从而减小多径效应对目标定位的干扰;最后引入目标位置的后验估计均值对目标位置进行修正,提高定位精度。实验结果表明,该定位算法具有可行性和有效性。

Object Device-Free Localization is allowed to localize and track person or other things without carrying any electronic device or tag. Aiming at the problem that the localization accuracy of radio tomographic imaging （RTI） algorithm is not ideal in a multipath environment, an improved algorithm based on bayesian background model was proposed. Firstly, the bayesian background model which is used to eliminate redundant links is established by combining the skew-laplace distribution with the bayesian theory. Then, the changes of received signal strength are weighted to reduce the interference of multipath effect on localization accuracy. Finally, the target location is corrected with the introduction of the posteriori estimate mean. The feasibility and availability of localization algorithm are verified by experiment.