基于Pearson相关系数的快速虚拟网格匹配定位算法

doi:10.11772/j.issn.1001-9081.2017071760

计算机应用 ›› 2018, Vol. 38 ›› Issue (3): 763-768.DOI: 10.11772/j.issn.1001-9081.2017071760

基于Pearson相关系数的快速虚拟网格匹配定位算法

郝德华, 关维国, 邹林杰, 焦萌

辽宁工业大学电子与信息工程学院, 辽宁锦州 121001

收稿日期:2017-07-18 修回日期:2017-09-07 出版日期:2018-03-10 发布日期:2018-03-07
通讯作者: 关维国
作者简介:郝德华(1992-),男,山东枣庄人,硕士研究生,主要研究方向:移动通信与无线技术;关维国(1973-),男,辽宁锦州人,教授,博士,主要研究方向:移动网络定位、泛在网络无线定位;邹林杰(1991-),女,河南新乡人,硕士研究生,主要研究方向:移动通信与无线技术;焦萌(1990-),女,河南商丘人,硕士研究生,主要研究方向:通信技术及其应用工程。
基金资助:
辽宁省自然科学基金指导计划项目（20170540437）。

Fast virtual grid matching localization algorithm based on Pearson correlation coefficient

HAO Dehua, GUAN Weiguo, ZOU Linjie, JIAO Meng

College of Electronic and Information Engineering, Liaoning University of Technology, Jinzhou Liaoning 121001, China

Received:2017-07-18 Revised:2017-09-07 Online:2018-03-10 Published:2018-03-07
Supported by:
This work is partially supported by the Directional Program of Liaoning Province Natural Science Foundation (20170540437).

摘要/Abstract

摘要： 针对室内环境下位置指纹匹配定位算法中离线数据库采集工作量较大的问题，提出了一种基于Pearson相关系数的快速虚拟网格匹配的定位算法。首先，将接收信号强度指示（RSSI）进行高斯滤波预处理得到接收信号强度向量；然后，利用Bounding-Box方法确定初始虚拟网格区域，将该网格区域快速迭代细分并计算网格中心点到各信标节点的距离对数向量，计算接收信号强度向量和距离对数向量之间的Pearson相关系数；最后，选取Pearson相关系数接近于-1的k个近邻坐标以相关系数加权估计确定待定位节点的最优估计位置。仿真实验结果表明，在1m虚拟网格且RSSI噪声标准差为3dBm的条件下，算法定位误差小于2m的概率大于94.2%，其定位精度优于位置指纹匹配算法，且无需建立RSSI指纹数据库，大大减少了定位工作量。

关键词: 室内定位, 信号强度, Bounding-Box, Pearson相关系数, 虚拟网格

Abstract: Focused on the issue that the location fingerprint matching localization algorithm has a large workload of offline database collection in an indoor environment, a fast virtual grid matching algorithm based on Pearson correlation coefficient was proposed. Firstly, the Received Signal Strength Indicator (RSSI) was preprocessed with Gaussian filter to obtain the received signal strength vector. Then, the Bounding-Box method was used to determine the initial virtual grid region. The grid region was rapidly and iteratively subdivided, the distance log vectors of the grid center point to beacon nodes were calculated, and the Pearson correlation coefficients between the received signal strength vector and the distance log vectors were calculated. Finally, the k nearest neighbor coordinates whose correlation coefficients close to -1 were selected, and the optimal estimation position of the undetermined node was determined by the weighted estimation of correlation coefficients. The simulation results show that the localization error of the proposed algorithm is less than 2m in 94.2% probability under the condition of 1m virtual grid and RSSI noise standard deviation of 3dBm. The positioning accuracy is better than that of the location fingerprint matching algorithm, and the RSSI fingerprint database is no longer needed, which greatly reduces the workload of localization.

Key words: indoor positioning, signal strength, Bounding-Box, Pearson correlation coefficient, virtual grid

中图分类号:

TP393.17

郝德华, 关维国, 邹林杰, 焦萌. 基于Pearson相关系数的快速虚拟网格匹配定位算法[J]. 计算机应用, 2018, 38(3): 763-768.

HAO Dehua, GUAN Weiguo, ZOU Linjie, JIAO Meng. Fast virtual grid matching localization algorithm based on Pearson correlation coefficient[J]. Journal of Computer Applications, 2018, 38(3): 763-768.

参考文献

[1] MANNUCCI A J, WILSON B D, YUAN D N, et al. A global mapping technique for GPS-derived ionospheric total electron content measurements[J]. Radio Science, 2016, 33(3):565-582.
[2] DENG Z, YU Y, YUAN X, et al. Situation and development tendency of indoor positioning[J]. China Communications, 2013, 10(3):42-55.
[3] 乐志伟,王浩,谢小军.基于RSSI障碍势能矫正的定位算法[J].电子测量与仪器学报,2017,31(2):200-207.(LE Z W, WANG H, XIE X J. Localization algorithm based on RSSI obstacles potential energy correction[J]. Journal of Electronic Measurement and Instrumentation, 2012, 9(2):52-65.)
[4] 毕京学,甄杰,郭英.室内定位无线接收信号强度测距模型的研究[J].导航定位学报,2014,2(4):8-10. (BI J X, ZHEN J, GUO Y. Indoor positioning range based model of the received signal strength[J]. Journal of Navigation and Positioning, 2014, 2(4):8-10.)
[5] 周艳.基于RSSI测距的传感器网络定位算法研究[J].计算机科学,2009,36(4):119-120.(ZHOU Y. Study of wireless sensor network localization algorithm based on RSSI[J]. Computer Science, 2009, 36(4):119-120.)
[6] 张鹏,周建国,冯欣,等.接收信号强度测距法无线室内定位解算方法研究[J].测绘科学,2014,39(4):13-16.(ZHANG P, ZHOU J G, FENG X, et al. RSSI ranging based wireless indoor positioning solutions[J]. Science of Surveying and Mapping, 2014, 39(4):13-16.)
[7] 关维国,鲁宝春.基于二维网格融合特征参数的室内匹配定位算法[J].计算机应用,2014,34(9):2464-2467.(GUAN W G, LU B C. Indoor matching localization algorithm based on two-dimensional grid characteristic parameter fusion[J]. Journal of Computer Applications, 2014, 34(9):2464-2467.)
[8] SUN Y L, XU Y B. Error estimation method for matrix correlation-based Wi-Fi indoor localization[J]. Ksii Transactions on Internet & Information Systems, 2013, 7(11):2657-2675.
[9] 王艳丽,杨如民,余成波,等.相关性匹配蓝牙信标位置指纹库的室内定位[J].电讯技术,2017,57(2):145-150.(WANG Y L, YANG R M, YU C B, et al. Indoor localization of bluetooth beacon position fingerprint based on correlation algorithm[J]. Telecommunication Engineering, 2017, 57(2):145-150.)
[10] WANG R, FENG J. Grid-based correlation localization method in mixed line-of-sight/non-line-of-sight environments[J]. Ksii Transactions on Internet & Information Systems, 2015, 9(1):87-107.
[11] 张乙竹,李雅晴,周礼争,等.基于自适应RSSI的Bounding-Box轮回选择WSN定位算法[J].计算机应用究,2016,33(9):2767-2768.(ZHANG Y Z, LI Y Q, ZHOU L Z, et al. Bounding-box recurrent selection algorithm for WSN based on self-adapted RSSI[J]. Application Research of Computers, 2016, 33(9):2767-2768.)
[12] 刘云,刘菁原.基于RSSI的加权质心定位算法优化研究[J]. 华中师范大学学报(自然科学版),2016,50(3):358-362.(LIU Y, LIU J Y. Optimization research on weighted centroid localization algorithm based on RSSI[J]. Journal of Huazhong Normal University (Natural Sciences), 2016, 50(3):358-362.)
[13] 胡晖,许浩峰,包伟华.基于相关性算法的超声波回波定位[J].自动化仪表,2015,36(10):96-98.(HU H, XU H F, BAO W H. Ultrasonic echo location based on correlation algorithm[J]. Process Automation Instrumentation, 2015, 36(10):96-98.)

[1]	栾佳宁, 张伟, 孙伟, 张奥, 韩冬. 基于二维码视觉与激光雷达融合的高精度定位算法[J]. 计算机应用, 2021, 41(5): 1484-1491.
[2]	张健铭, 施元昊, 徐正蓺, 魏建明. 基于误差预测的自适应UWB/PDR融合定位算法[J]. 计算机应用, 2020, 40(6): 1755-1762.
[3]	张榜, 朱金鑫, 徐正蓺, 刘盼, 魏建明. 基于贝叶斯网络的楼层定位算法[J]. 计算机应用, 2019, 39(8): 2468-2474.
[4]	周非, 夏鹏程. 基于主成分分析和卡方距离的信号强度差指纹定位算法[J]. 计算机应用, 2019, 39(5): 1405-1410.
[5]	丁建立, 穆涛, 王怀超. 基于谱回归核判别分析的候机楼室内快速定位算法[J]. 计算机应用, 2019, 39(1): 256-261.
[6]	阮琨, 王玫, 罗丽燕, 熊璐琦, 宋浠瑜. 基于智能手机的地磁/WiFi/PDR的室内定位算法[J]. 计算机应用, 2018, 38(9): 2598-2602.
[7]	汪明, 许亮, 何小敏. 无线传感器网络精度优选RSSI协作定位算法[J]. 计算机应用, 2018, 38(7): 1981-1988.
[8]	刘盼, 张榜, 黄超, 杨卫军, 徐正蓺. 室内环境约束的行人航向粒子滤波修正方法[J]. 计算机应用, 2018, 38(12): 3360-3366.
[9]	张涛, 马磊, 梅玲玉. 基于单目视觉的仓储物流机器人定位方法[J]. 计算机应用, 2017, 37(9): 2491-2495.
[10]	卞佳兴, 朱荣, 陈玄. 基于改进双向测距到达时间差定位算法的超宽带定位系统[J]. 计算机应用, 2017, 37(9): 2496-2500.
[11]	李丽娜, 李文浩, 尤洪祥, 王越. 回溯搜索优化改进矩阵填充的高效位置指纹库构建[J]. 计算机应用, 2017, 37(7): 1893-1899.
[12]	张明洋, 陈剑, 闻英友, 赵宏, 王玉刚. 基于动态时间规整距离指纹匹配的Wi-Fi网络室内定位算法[J]. 计算机应用, 2017, 37(6): 1550-1554.
[13]	韦春玲, 王步飞. 基于WLAN接收信号强度特征的室内活动识别[J]. 计算机应用, 2017, 37(5): 1326-1330.
[14]	王续乔, 王瑾琨. 基于D-S证据理论的室内组合定位算法[J]. 计算机应用, 2017, 37(4): 1198-1201.
[15]	李新春, 侯跃. 基于改进AP选择和K最近邻法算法的室内定位技术[J]. 计算机应用, 2017, 37(11): 3276-3280.

基于Pearson相关系数的快速虚拟网格匹配定位算法

Fast virtual grid matching localization algorithm based on Pearson correlation coefficient

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics