唐恒亮,米 源,刘 涛,薛 菲,杨 玺.基于空间位置约束的稀疏指纹室内定位方法[J].电子测量与仪器学报,2020,34(6):79-85
基于空间位置约束的稀疏指纹室内定位方法
Sparse fingerprint indoor localization based on spatial position constraint
  
DOI:
中文关键词:  室内定位  接收信号强度  稀疏指纹  空间位置约束
英文关键词:indoor localization  received signal strength  sparse fingerprint  spatial position constraint
基金项目:国家自然科学基金( 61803035)、北京市“ 高创计划” 青年拔尖个人( 2017000026833ZK25)、北京市通州区运河计划领军人才(YHLB2017038)、北京物资学院基层学术团队建设“北京市智能物流系统协同创新中心”(2019XJJCTD04)资助项目
作者单位
唐恒亮 1.北京物资学院信息学院 
米 源 1.北京物资学院信息学院 
刘 涛 1.北京物资学院信息学院 
薛 菲 1.北京物资学院信息学院 
杨 玺 1.北京物资学院信息学院 
AuthorInstitution
Tang Hengliang 1.School of Information, Beijing Wuzi University 
Mi Yuan 1.School of Information, Beijing Wuzi University 
Liu Tao 1.School of Information, Beijing Wuzi University 
Xue Fei 1.School of Information, Beijing Wuzi University 
Yang Xi 1.School of Information, Beijing Wuzi University 
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中文摘要:
      针对基于位置服务的实际应用需求,分析了现有室内定位技术的局限性,提出一种基于空间位置约束的稀疏指纹定位 方法,在数据层有效融合惯导和无线局域网(WLAN)定位信息,充分发挥二者优势协同完成定位任务。 首先利用 WLAN 提供的 接收信号强度(RSS)信息构建空间位置指纹数据库,并基于 RSS 构建稀疏指纹表征与定位模型;鉴于 RSS 数据易受环境干扰呈 现多变性,利用惯导技术对位移状态进行初步估计,并以此作为约束条件构建基于空间位置约束的稀疏指纹定位模型。 仿真实 验结果表明,所提方法较惯导和稀疏指纹方法在定位精度方面分别提升 58%和 33%。
英文摘要:
      For the practical application requirements of location-based services, a sparse fingerprint localization method based on spatial position constraint is proposed, after fully analyzing the limitations of the existing indoor location technologies. The positioning information from inertial navigation system (INS) and wireless local area network (WLAN) are deeply integrated on the data level, to coordinate the positioning task. Based on the received signal strength (RSS) data provided by WLAN, the spatial-location-fingerprint database is constructed, together with the sparse fingerprint representation and location model. In view of the RSS variability due to environmental interferences, the displacement state can be preliminarily estimated by INS, which will be as a constraint condition to construct the sparse fingerprint location model based on spatial position constraint. The simulation experimental results show that the positioning accuracy of this method is improved by 58% and 33% respectively, compared with the INS and sparse fingerprint methods. It is demonstrated that the proposed model can appropriately compensate the accumulative error of INS, and the motion prediction by INS also can restrict the jumping and distortion effects of RSS signals to a certain extent.
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