高精度室内定位研究评述及未来演进展望

doi:10.11959/j.issn.1000-436x.2021136

Abstract

Abstract:

Nowadays, high-precision indoor positioning service has witnessed an increasing interest because it has become the key support of many 5G major application scenarios, and the implementation technologies and mechanisms are continuously updated with advantages, disadvantages and specificities.Therefore, a timely and systematic summary of indoor-positioning was made, and that of prospects for where this domain was heading.Firstly, based on the analysis of the existing papers, summarization of this domain was proposed from two aspects, such as positioning-techniques and positioning-methods.Secondly, a classification model was put forward based on the positioning scenarios and the “hidden requirements” included were pointed out.Then, the evaluation indices system of positioning-systems was proposed and some of the existing high-accuracy positioning system was evaluated by it.Finally, the direction of evolution about high-precision indoor positioning domain was summarized, the development trend of integrated network was pointed out, and some important concepts, such as “online is inplace”, were proposed.

Key words: positioning and navigation, indoor positioning, 5G, integrated network, positioning technology

CLC Number:

TP393

Huiqiang WANG, Kaixuan GAO, Hongwu LYU. Survey of high-precision localization and the prospect of future evolution[J]. Journal on Communications, 2021, 42(7): 198-210.

Figures/Tables 11

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匹配方法	方法类型	优点	缺点
KNN	确定型算法	不需要训练、易于实现、性能良好、应用广泛	匹配过程需要大量算力，定位结果对K取值敏感，需反复实验
极大似然估计法^[35]	概率型算法	使用统计模型参数估计提升精度	需要专业知识预判概率模型，复杂环境难以应对单一概率模型
朴素贝叶斯算法	概率型算法	使每个定位点具有最大后验概率，进一步提升定位精度	需在每一个网格内重复采集大量信号
SVM算法	智能算法	适应于“指纹-位置”的非线性映射关系	分类效果对核函数的选择敏感，核函数难以选取
神经网络算法^[36]	智能算法	定位精度较高、误差较小、不需要考虑传播模型和概率模型	需要复杂的参数调整过程，训练耗时长、代价高

定位原理	定位技术	实现逻辑	优点	缺点
	ToF/ToA	测量信号单程传播时间，换算成距离，完成定位解算	结构简单、易于实现	依赖基站间高精度时间同步，需要待测节点与基站时间同步，受NLOS误差影响
基于时间测距	TDoA	测量2个基站的信号传播时差，换算成距离差，完成定位解算	结构简单、易于实现，待测节点不需时间同步	依赖基站间高精度时间同步，受NLOS误差影响
	RToF	测量信号在基站与终端间的往返时间，换算成距离，完成定位	结构简单、易于实现，进一步降低同步要求	受NLOS误差影响，需双向通信，网络通信开销导致额外误差，系统容量有限
基于传播模型测距	RSSI	通过信号衰减模型将信号强度换算为传播距离，完成定位解算	结构简单、易于实现，不需要时间同步	受NLOS误差影响更严重
基于相位测角	AoA/AoD	测量不同阵元上的相位差，换算成信号到达角度，完成定位	不需要时间同步，近距离定位精度高	需要阵列天线，增加硬件成本，解算复杂度高，受NLOS误差影响
基于指纹匹配	CSI	离线采集指纹、在线匹配指纹，直接搜索得到终端位置	不需要时间同步，具备一定抗NLOS能力，在线定位阶段复杂度低	离线训练阶段耗时长、成本高，环境改变后定位精度显著下降

定位场景	配合度	定位解算	源数据	兼顾通信	算力要求	功耗	容量	隐私性
WAI	高	用户完成	用户独有	不需要	高	高	无限制	高
WAU	高	双方完成	双方共有	需要	高	高	高	中
WIH	无	服务商提供	服务商独有	需要	低	低	低	低

定位系统	适用类别	适用场景	定位精度	部署成本	运维成本	实时性	覆盖范围	稳健性	一体化程度
5G蜂窝网定位系统	我在哪你在哪	广域下行	亚米级	与通信网共用	低	高	广域	高	设备级
DeepFi	他在哪	局域上行	米级	中等	高	中	室内	低	无
Widar3.0	他在哪	局域静默	厘米级	高	高	高	室内	低	无
共频带定位系统	我在哪	广域下行	亚米级	中等	低	高	广域	高	应用级
Ubisense	我在哪	局域下行	厘米级	高	低	中	室内	高	无
UWB-IP	我在哪	局域下行	厘米级	高	低	高	室内	高	无

Survey of high-precision localization and the prospect of future evolution

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