基于CSI张量分解的室内Wi-Fi指纹定位方法
Indoor Wi-Fi fingerprint localization method based on CSI tensor decomposition
- 通信学报 2021年42卷第11期 页码：159-171
- 作者机构：
1. 重庆邮电大学通信与信息工程学院，重庆 400065
2. 重庆邮电大学移动通信技术重庆市重点实验室，重庆 400065
- 作者简介：
[ "周牧（1984− ），男，四川自贡人，博士，重庆邮电大学教授、博士生导师，主要研究方向为无线定位与感知、量子精密测量、多源信息融合与机器学习等" ]
[ "龙玥辛（1998− ），女，四川宜宾人，重庆邮电大学硕士生，主要研究方向为室内无线定位、数据处理与深度学习等" ]
[ "蒲巧林（1988− ），女，四川遂宁人，博士，重庆邮电大学讲师、硕士生导师，主要研究方向为室内定位、位置隐私、网络优化等" ]
[ "王勇（1987− ），男，云南邵通人，博士，重庆邮电大学讲师、硕士生导师，主要研究方向为室内定位、深度学习等" ]
[ "何维（1995− ），女，四川南充人，重庆邮电大学博士生，主要研究方向为机器学习、人机交互技术等" ]
- 基金信息：重庆市教委科学技术研究基金资助项目(KJZD-K202000605);重庆市教委科学技术研究基金资助项目(KJQN201900603);重庆市自然科学基金资助项目(cstc2020jcyj-msxmX0842);重庆市自然科学基金资助项目(cstc2020jcyj-msxmX0865);国家自然科学基金资助项目(61901076);国家自然科学基金资助项目(61704015);重庆市研究生科研创新基金资助项目(CYS21293)
DOI：10.11959/j.issn.1000-436x.2021218
中图分类号： TN92-
网络首发：2021-11，
纸质出版：2021-11-25
- 稿件说明：
移动端阅览
周牧, 龙玥辛, 蒲巧林, 等. 基于CSI张量分解的室内Wi-Fi指纹定位方法[J]. 通信学报, 2021,42(11):159-171.
Mu ZHOU, Yuexin LONG, Qiaolin PU, et al. Indoor Wi-Fi fingerprint localization method based on CSI tensor decomposition[J]. Journal on Communications, 2021, 42(11): 159-171.
周牧, 龙玥辛, 蒲巧林, 等. 基于CSI张量分解的室内Wi-Fi指纹定位方法[J]. 通信学报, 2021,42(11):159-171. DOI： 10.11959/j.issn.1000-436x.2021218.
Mu ZHOU, Yuexin LONG, Qiaolin PU, et al. Indoor Wi-Fi fingerprint localization method based on CSI tensor decomposition[J]. Journal on Communications, 2021, 42(11): 159-171. DOI： 10.11959/j.issn.1000-436x.2021218.
摘要
针对指纹库规模的增大导致CSI指纹的训练成本和处理复杂性显著增加的问题，提出了一种基于CSI张量分解的室内Wi-Fi指纹定位方法。首先，将基于平行因子分析模型的张量分解算法和交替最小二乘迭代算法相结合以减少环境噪声的干扰；其次，利用张量小波分解算法对降噪后的张量进行特征提取以得到CSI位置指纹；最后，基于偏最小二乘回归算法建立定位模型以实现位置估计。实验结果表明，所提算法在定位误差4 m内的置信概率为94.88%，验证了其在拟合CSI 位置指纹和位置坐标关系的同时具有较好的定位性能。
Abstract
Aiming at the problem that as the scale of the fingerprint database increases
the training cost and processing complexity of CSI fingerprints will also greatly increase
an indoor Wi-Fi fingerprint localization method based on CSI tensor decomposition was proposed.Firstly
the tensor decomposition algorithm based on the PARAFAC (parallel factor) analysis model and the ALS (alternate least squares) iterative algorithm were combined to reduce the interference of the environment.Then
the tensor wavelet decomposition algorithm was used to extract the feature and obtain the CSI fingerprint.Finally
a localization model was established based on the PLSR (partial least squares regression) algorithm to realize the location estimation.Experimental results show that the confidence probability of the proposed method is 94.88% within 4 m localization error
which verifies that the proposed method has good localization performance while fitting the relationship between CSI location fingerprints and location coordinates.
关键词
Keywords
references
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