王超1,
杨乐3,
孙顺远1
1.江南大学轻工过程先进控制教育部重点实验室 无锡 214122
2.南京航空航天大学电磁频谱空间认知动态系统工信部重点实验室 南京 211106
3.坎特伯雷大学电气与计算机工程系 克赖斯特彻奇 8011
基金项目:国家自然科学基金 (61702228, 61803183)，江苏省自然基金 (BK20170198, BK20180591)，电磁频谱空间认知动态系统工信部重点实验室开放研究基金(KF20202104)
详细信息
作者简介:秦宁宁：1980年生，教授，博士，研究方向无线传感器网络的研究
王超：1995年生，硕士生，研究方向为室内定位
杨乐：1979年生，副教授，研究方向为无线信号统计与应用
孙顺远：1984年生，副教授，研究方向为无线传感网
通讯作者:秦宁宁 ningning801108@163.com
中图分类号:TN911.7; TP391计量
文章访问数:359
HTML全文浏览量:127
PDF下载量:26
被引次数:0
出版历程
收稿日期:2020-03-31
修回日期:2020-08-24
网络出版日期:2020-09-03
刊出日期:2021-06-18
Off Line Fingerprint Database Based on Multivariate Gaussian Mixture Model
Ningning QIN1, 2,,,Chao WANG1,
Le YANG3,
Shunyuan SUN1
1. Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China
2. Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
3. Department of Electrical and Computer Engineering, University of Canterbury, Christchurch 8011, New Zealand
Funds:The National Natural Science Foundation of China (61702228, 61803183), The Natural Science Foundation of Jiangsu Province (BK20170198, BK20180591), The Open Fund of Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space of Ministry of Industry and Information Technology(KF20202104)
摘要
摘要:针对室内环境下单次采样测量值的波动变化及信号间的相互干扰，该文提出一种基于分区多元高斯混合模型(MVGMM)的室内定位系统。根据信号接入点(AP)铺设位置与空间结构，系统采用一对多支持向量机算法对目标区域做分区操作，以精确信号变化的区域范围。利用狭小分区内信号间的耦合关系，建立基于信号间相互干扰的多元高斯混合模型，以改善信号波动所造成的定位精度下降。当室内环境发生变化时，基于分区多元高斯混合模型的自适应更新算法可对各分区指纹数据的可信度做出判断，并以自适应算法更新信号波动较大分区的模型参数，提高模型与现有环境间的耦合程度。实验结果表明，该文算法可利用相对少量样本数据，构建稳定可维护的室内信号分布模型，相较于其他算法，其定位精度也有一定程度提高。
关键词:室内定位/
多元高斯混合模型/
分区/
自适应更新
Abstract:For the fluctuation of single sampling measurement value and the mutual interference between signals in indoor environment, this paper proposes an indoor positioning system based on the partition MultiVariate Gaussian Mixture Model(MVGMM). According to the Access Point (AP) position and indoor spatial structure, the system uses SVM classification in “one-against-all” form to partition the target area in order to predict the subarea with signal changes. A MVGMM based on the mutual interference between signals is established by using the coupling relationship between multiple communication devices in the partition. It is important to improve the positioning accuracy which is affected by signal fluctuation. When the indoor environment changes, the adaptive updating algorithm based on the partition MVGMM can test the reliability of fingerprint data in each segmentation. Moreover, it can update the model parameters in the partition with large signal fluctuation by the adaptive algorithm to strengthen the coupling relationship between the model and the existing environment. Experimental result demonstrates that the proposed algorithm can build a stable and maintainable indoor signal distribution model by using a relatively small number of sample data. Its positioning accuracy is also improved to a certain extent compared to other algorithms.
Key words:Indoor positioning/
MultiVariate Gaussian Mixture Model(MVGMM)/
Subarea/
Adaptive algorithm
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