空间交替广义期望最大化(SAGE)算法可以高效地实现室内信道多径参数的估计,同时提供相对较高的精度。该文提出一种基于多径指纹的概率匹配室内定位方法,将SAGE算法得到的信道多径参数作为指纹数据进行定位,以求获得更好的定位精度和鲁棒性。通过分析典型场景下的定位实验测量结果,并与同类型的传统室内定位方法进行比较,验证了该方法的有效性。
Abstract
The space-alternating generalized expectation-maximization (SAGE) algorithm provides efficient, accurate indoor channel multipath estimation. This paper describes a multipath-based probabilistic fingerprinting method for indoor positioning that utilizes the channel multipath parameters obtained by the SAGE algorithm as fingerprinting data to achieve better positioning accuracy and robustness. Tests of measurements in a typical indoor environment show that this method is more accurate than traditional indoor positioning methods.
关键词
信道多径估计 /
指纹 /
室内定位 /
多径参数
Key words
channel multipath estimation /
fingerprinting /
indoor positioning /
multipath parameters
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