基于向量相似性的多维标度定位算法

Multidimensional scaling localization algorithm based on vector similarity

为解决室内定位环境复杂、传播信号易受干扰,导致室内定位算法定位误差较大的问题,提出一种基于向量相似性的多维标度定位算法。将向量相似性特征和相关性修正模型融入多维标度算法框架,引入cosine指标表征信号向量间的相似度,为节点相关性提供度量标准,提出一种基于向量样本熵的相关性修正模型进一步优化节点间的相似性矩阵。仿真结果表明,该算法可以有效获得目标节点的位置信息,提高节点的定位精度,降低复杂室内环境对无线传感器信号的影响。

To solve the problems that the indoor positioning environment is complex and the propagation signal is susceptible to interference,which lead to large positioning error of indoor positioning algorithm,a multi-dimensional scaling algorithm based on vector similarity was proposed.The vector similarity feature and correlation correction model were integrated into the multidimensional scaling algorithm framework,the cosine index was introduced to characterize the similarity between signal vectors,metrics were provided for node correlation,and a correlation based on vector sample entropy was proposed.The similarity matrix between nodes was further optimized.Experimental results show that the proposed algorithm can effectively obtain the position information of the target node,improve the positioning accuracy of the node,and reduce the impact of complex indoor environment on wireless sensor signals.