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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。
Spatial Alternate Generalized Expectation Maximization (SAGE) algorithm can efficiently estimate indoor channel multipath parameters while providing relatively high accuracy. This paper presents a probabilistic matching indoor positioning method based on multipath fingerprinting. The channel multipath parameters obtained by the SAGE algorithm are used as fingerprint data to obtain better positioning accuracy and robustness. The effectiveness of this method is verified by analyzing the results of the location experiment in a typical scenario and comparing with the traditional indoor positioning methods of the same type.