基于半张量积压缩感知的室内定位算法  

Indoor localization based on semi-tensor product compression sensing

作　　者：蒲巧林 周龙璨 周牧 蒋逢怡 李云海 Pu Qiaolin;Zhou Longcan;Zhou Mu;Jiang Fengyi;Li Yunhai(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《仪器仪表学报》2024年第12期329-339,共11页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金项目(62201110);重庆市教委科学技术研究中心项目(KJQN202200648);重庆市科委项目(CSTB2022NSCQ-MSX1385)资助。

摘　　要：为应对大场景环境下利用压缩感知(CS)技术实现无线局域网(WLAN)的室内定位时,所面临的定位精度降低和计算复杂度提升的两大挑战,首先引入了改进的聚类算法进行粗定位,以此有效缩减搜索范围。针对无线信号的奇异值问题,创新性地提出了自适应直觉模糊C有序均值聚类算法。其次,为克服高维观测矩阵带来的巨大存储压力,提出了一种半张量积压缩感知(STP-CS)技术对观测矩阵降维。相比传统CS方法,该方法能在维持相同维度的基础上,容纳更多数量的接入点。实验结果表明,所提的算法在保证定位精度的前提下,成倍降低观测矩阵所需的存储空间,显著降低计算开销,在大场景应用中更具优势。When using the compression sensing(CS)localization algorithm for large scene environments based on the wireless local area network(WLAN),there are two challenges:reduced positioning accuracy and increased computational complexity.To address them,this paper first introduces an improved clustering algorithm for coarse localization to reduce the search range.Specifically,for the singular value problem of wireless signals,we innovatively propose the adaptive intuitionistic fuzzy c-ordered mean clustering algorithm.Secondly,to overcome the high storage pressure brought by the high-dimensional observation matrix,a semi-tensor product compression sensing(STP-CS)technique is proposed.Compared with the traditional CS method,this method can accommodate more access points while maintaining the same dimensionality.Experimental results show that the algorithm proposed in this paper significantly reduces the storage space required by the observation matrix and the computational overhead under the premise of ensuring positioning accuracy,so it is more advantageous in large scene applications.When using the compression sensing(CS)localization algorithm for large scene environments based on the wireless local area network(WLAN),two challenges arise:reduced positioning accuracy and increased computational complexity.To address these issues,this paper introduces an improved clustering algorithm for coarse localization to reduce the search range.Specifically,for the singular value problem of wireless signals,we innovatively propose the adaptive intuitionistic fuzzy c-ordered mean clustering algorithm.Secondly,to overcome the high storage pressure brought by the high-dimensional observation matrix,a semi-tensor product compression sensing(STP-CS)technique is proposed.Compared with the traditional CS method,this method can accommodate more access points while maintaining the same dimensionality.Experimental results show that the proposed algorithm significantly reduces the storage space required by the observation matrix and decreases

关 键 词：室内定位 半张量积压缩感知 聚类 接入点 观测矩阵 

分 类 号：TN91[电子电信—通信与信息系统] TH89[电子电信—信息与通信工程]