Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (12): 3947-3954.DOI: 10.11772/j.issn.1001-9081.2023010005
• Frontier and comprehensive applications • Previous Articles Next Articles
Haiyong ZHANG1,2, Xianjin FANG1(), Enwan ZHANG3, Baoyu LI2, Chao PENG4, Jianxiang MU2
Received:
2023-01-04
Revised:
2023-04-23
Accepted:
2023-04-24
Online:
2023-06-06
Published:
2023-12-10
Contact:
Xianjin FANG
About author:
ZHANG Haiyong, born in 1992, M. S. candidate. His research interests include operator big data, machine learning.Supported by:
张海永1,2, 方贤进1(), 张恩皖3, 李宝玉2, 彭超4, 穆健翔2
通讯作者:
方贤进
作者简介:
张海永(1992—),男,安徽合肥人,硕士研究生,主要研究方向:运营商大数据、机器学习基金资助:
CLC Number:
Haiyong ZHANG, Xianjin FANG, Enwan ZHANG, Baoyu LI, Chao PENG, Jianxiang MU. Fingerprint positioning method based on measurement report signal clustering[J]. Journal of Computer Applications, 2023, 43(12): 3947-3954.
张海永, 方贤进, 张恩皖, 李宝玉, 彭超, 穆健翔. 基于测量报告信号聚类的指纹定位方法[J]. 《计算机应用》唯一官方网站, 2023, 43(12): 3947-3954.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023010005
符号 | 说明 |
---|---|
MR | 测量报告 |
CGI | 主、邻基站标识, |
TA | 时间提前量 |
RSRP | 主、邻基站信号强度(参考信号接收功率) |
LON | 经度 |
LAT | 纬度 |
H | 基站的高度 |
S | 基站与手机终端的水平距离 |
d | 基站与手机终端的欧氏距离 |
基站与手机终端距离的理论最小值和最大值 | |
Min_Ts、Max_Ts | TA值对应的最小和最大采样时间,可以计算TA值对应的最小和最大距离 |
r | 采样点合理率,用来评判样本数据准确性 |
p | 子栅格内MR样本缺失基站CGI占该区域内 样本的比例 |
AVG_RSRP | 子栅格内基站信号强度的平均值 |
w | 填补MR样本点内无基站信号强度的权重 |
预设值,表示p的范围 | |
预设值,p不同范围对应的信号强度权重值 | |
ADD_RSRP | 填补MR样本点内无对应基站电平值 |
GRID_ID | 划分栅格ID |
GRID_SUB_ID | 划分子栅格ID |
GRID_SUB_TYPE | 子栅格类型(室内、道路和室外) |
GRID_SUB_ORDER | 封闭子栅格经纬度序列 |
平均定位精度 | |
指纹库定位与实际位置的距离偏差 | |
分别代表定位误差在25 m、50 m、75 m、100 m 范围内的点数 | |
分别代表定位误差在25 m、50 m、75 m、100 m 范围内的占比 | |
GROUP_ID | 虚拟子区域ID(子区域聚类中心ID) |
Tab. 1 Explanation of symbols used in this paper
符号 | 说明 |
---|---|
MR | 测量报告 |
CGI | 主、邻基站标识, |
TA | 时间提前量 |
RSRP | 主、邻基站信号强度(参考信号接收功率) |
LON | 经度 |
LAT | 纬度 |
H | 基站的高度 |
S | 基站与手机终端的水平距离 |
d | 基站与手机终端的欧氏距离 |
基站与手机终端距离的理论最小值和最大值 | |
Min_Ts、Max_Ts | TA值对应的最小和最大采样时间,可以计算TA值对应的最小和最大距离 |
r | 采样点合理率,用来评判样本数据准确性 |
p | 子栅格内MR样本缺失基站CGI占该区域内 样本的比例 |
AVG_RSRP | 子栅格内基站信号强度的平均值 |
w | 填补MR样本点内无基站信号强度的权重 |
预设值,表示p的范围 | |
预设值,p不同范围对应的信号强度权重值 | |
ADD_RSRP | 填补MR样本点内无对应基站电平值 |
GRID_ID | 划分栅格ID |
GRID_SUB_ID | 划分子栅格ID |
GRID_SUB_TYPE | 子栅格类型(室内、道路和室外) |
GRID_SUB_ORDER | 封闭子栅格经纬度序列 |
平均定位精度 | |
指纹库定位与实际位置的距离偏差 | |
分别代表定位误差在25 m、50 m、75 m、100 m 范围内的点数 | |
分别代表定位误差在25 m、50 m、75 m、100 m 范围内的占比 | |
GROUP_ID | 虚拟子区域ID(子区域聚类中心ID) |
TA值 | Min_Ts / Ts | Max_Ts / Ts |
---|---|---|
0 | 0 | 16 |
1 | 16 | 32 |
︙ | ︙ | ︙ |
11 | 176 | 192 |
12 | 192 | 224 |
︙ | ︙ | ︙ |
37 | 992 | 1 024 |
38 | 1 024 | 1 280 |
︙ | ︙ | ︙ |
41 | 1 792 | 2 048 |
42 | 2 048 | 3 072 |
43 | 3 072 | 4 096 |
44 | 4 096 | ︙ |
Tab. 2 TA relations
TA值 | Min_Ts / Ts | Max_Ts / Ts |
---|---|---|
0 | 0 | 16 |
1 | 16 | 32 |
︙ | ︙ | ︙ |
11 | 176 | 192 |
12 | 192 | 224 |
︙ | ︙ | ︙ |
37 | 992 | 1 024 |
38 | 1 024 | 1 280 |
︙ | ︙ | ︙ |
41 | 1 792 | 2 048 |
42 | 2 048 | 3 072 |
43 | 3 072 | 4 096 |
44 | 4 096 | ︙ |
实验序号 | 方案A | 方案B | 方案C | 方案D |
---|---|---|---|---|
均值 | 52.33 | 53.32 | 48.44 | 44.73 |
1 | 52.66 | 53.62 | 48.50 | 43.79 |
2 | 52.33 | 52.59 | 48.68 | 44.42 |
3 | 52.64 | 53.89 | 47.95 | 45.77 |
4 | 51.69 | 52.85 | 49.01 | 45.38 |
5 | 51.73 | 53.38 | 48.31 | 44.35 |
6 | 52.93 | 53.59 | 48.19 | 44.67 |
Tab.4 Statistics of APE
实验序号 | 方案A | 方案B | 方案C | 方案D |
---|---|---|---|---|
均值 | 52.33 | 53.32 | 48.44 | 44.73 |
1 | 52.66 | 53.62 | 48.50 | 43.79 |
2 | 52.33 | 52.59 | 48.68 | 44.42 |
3 | 52.64 | 53.89 | 47.95 | 45.77 |
4 | 51.69 | 52.85 | 49.01 | 45.38 |
5 | 51.73 | 53.38 | 48.31 | 44.35 |
6 | 52.93 | 53.59 | 48.19 | 44.67 |
方案 | 总点数 | ||||||||
---|---|---|---|---|---|---|---|---|---|
A | 62 401 | 22 000 | 42 789 | 51 356 | 55 466 | 35.26 | 68.57 | 82.30 | 88.89 |
B | 62 401 | 27 356 | 40 756 | 48 784 | 53 252 | 43.84 | 65.31 | 78.18 | 85.34 |
C | 62 401 | 32 346 | 42 823 | 50 291 | 54 056 | 51.84 | 68.63 | 80.59 | 86.63 |
D | 62 401 | 32 849 | 44 436 | 51 645 | 55 017 | 52.64 | 71.21 | 82.76 | 88.17 |
Tab.5 Analysis of error results of different schemes
方案 | 总点数 | ||||||||
---|---|---|---|---|---|---|---|---|---|
A | 62 401 | 22 000 | 42 789 | 51 356 | 55 466 | 35.26 | 68.57 | 82.30 | 88.89 |
B | 62 401 | 27 356 | 40 756 | 48 784 | 53 252 | 43.84 | 65.31 | 78.18 | 85.34 |
C | 62 401 | 32 346 | 42 823 | 50 291 | 54 056 | 51.84 | 68.63 | 80.59 | 86.63 |
D | 62 401 | 32 849 | 44 436 | 51 645 | 55 017 | 52.64 | 71.21 | 82.76 | 88.17 |
方案 | 平均误差 | 中值误差 | 90%误差 |
---|---|---|---|
A | 52.33 | 32.16 | 106.85 |
B | 53.32 | 31.39 | 134.95 |
C | 48.44 | 23.19 | 124.66 |
D | 44.73 | 22.00 | 113.92 |
Tab.6 MR positioning results of different schemes
方案 | 平均误差 | 中值误差 | 90%误差 |
---|---|---|---|
A | 52.33 | 32.16 | 106.85 |
B | 53.32 | 31.39 | 134.95 |
C | 48.44 | 23.19 | 124.66 |
D | 44.73 | 22.00 | 113.92 |
日期 | 时间/min | 日期 | 时间/min |
---|---|---|---|
2023-03-01 | 35 | 2023-03-05 | 49 |
2023-03-02 | 51 | 2023-03-06 | 40 |
2023-03-03 | 52 | 2023-03-07 | 46 |
2023-03-04 | 57 |
Tab. 7 Analysis of MR positioning efficiency
日期 | 时间/min | 日期 | 时间/min |
---|---|---|---|
2023-03-01 | 35 | 2023-03-05 | 49 |
2023-03-02 | 51 | 2023-03-06 | 40 |
2023-03-03 | 52 | 2023-03-07 | 46 |
2023-03-04 | 57 |
1 | DEL PERAL-ROSADO J D, RAULEFS R, LÓPEZ-SALCEDO J A, et al. Survey of cellular mobile radio localization methods: from 1G to 5G[J]. IEEE Communications Surveys and Tutorials, 2018, 20(2):1124-1148. 10.1109/comst.2017.2785181 |
2 | 武青. 基于指纹库和无线测量报告的用户定位系统[D]. 北京:北京邮电大学, 2021:1-2. |
WU Q. User localization system based on fingerprint database and measurement report[D]. Beijing: Beijing University of Posts and Telecommunications, 2021:1-2. | |
3 | 康军,黄山,段宗涛,等.时空轨迹序列模式挖掘方法综述[J]. 计算机应用, 2021, 41(8): 2379-2385. 10.11772/j.issn.1001-9081.2020101571 |
KANG J, HUANG S, DUAN Z T, et al. Review of spatio-temporal trajectory sequence pattern mining methods[J]. Journal of Computer Applications, 2021, 41(8): 2379-2385. 10.11772/j.issn.1001-9081.2020101571 | |
4 | 元广杰,李小东,江照意,等.路测数据驱动的移动终端定位方法[J]. 计算机应用, 2016, 36(12): 3515-3520. 10.11772/j.issn.1001-9081.2016.12.3515 |
YUAN G J, LI X D, JIANG Z Y, et al. Mobile terminal positioning method driven by road test data[J]. Journal of Computer Applications, 2016, 36(12): 3515-3520. 10.11772/j.issn.1001-9081.2016.12.3515 | |
5 | 张齐林,李方伟,王明月.基于时间反演的到达时间定位[J]. 计算机应用, 2021, 41(3): 820-824. 10.16798/j.issn.1003-0530.2021.05.020 |
ZHANG Q L, LI F W, WANG M Y. Time of arrival positioning based on time reversal[J]. Journal of Computer Applications, 2021, 41(3): 820-824. 10.16798/j.issn.1003-0530.2021.05.020 | |
6 | LIU Y, GUO F. Performance analysis of TDOA and FDOA estimation for pulse signals[J]. International Journal of Antennas and Propagation, 2022, 2022: No.7672417. 10.1155/2022/7672417 |
7 | DÍEZ-GONZÁLEZ J, ÁVAREZ R, VERDE P, et al. Analysis of reliable deployment of TDOA local positioning architectures[J]. Neurocomputing, 2022, 484: 149-160. 10.1016/j.neucom.2021.12.074 |
8 | CHANG A C, CHANG J C. Robust mobile location estimation using hybrid TOA/AOA measurements in cellular systems[J]. Wireless Personal Communications, 2012, 65(1): 1-13. 10.1007/s11277-011-0224-8 |
9 | LI Y Y, QI G Q, SHENG A D. Performance metric on the best achievable accuracy for hybrid TOA/AOA target localization[J]. IEEE Communications Letters, 2018, 22(7): 1474-1477. 10.1109/lcomm.2018.2833544 |
10 | ZHOU M, LI Y, TAHIR M J, et al. Integrated statistical test of signal distributions and access point contributions for Wi-Fi indoor localization[J]. IEEE Transactions on Vehicular Technology, 2021, 70(5): 5057-5070. 10.1109/tvt.2021.3076269 |
11 | WANG B, GAN X, LIU X, et al. A novel weighted KNN algorithm based on RSS similarity and position distance for Wi-Fi fingerprint positioning [J]. IEEE Access, 2020, 8: 30591-30602. 10.1109/access.2020.2973212 |
12 | VO Q D, DE P. A survey of fingerprint-based outdoor localization[J]. IEEE Communications Surveys and Tutorials, 2016, 18(1): 491-506. 10.1109/comst.2015.2448632 |
13 | ZHANG L, CHU X, ZHAI M. Machine learning-based integrated wireless sensing and positioning for cellular network[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: No.5501011. 10.1109/tim.2022.3224513 |
14 | ZHANG Y, RAO W, YUAN M, et al. Context-aware telco outdoor localization [J]. IEEE Transactions on Mobile Computing, 2022, 21(4): 1211-1225. 10.1109/tmc.2020.3025127 |
15 | ZHANG Y, DING A Y, OTT J, et al. Transfer learning-based outdoor position recovery with cellular data [J]. IEEE Transactions on Mobile Computing, 2021, 20(5): 2094-2110. 10.1109/tmc.2020.2968899 |
16 | 王宁,刘旭峰,贾元启,等. 基于机器学习的LTE-MR定位算法研究与应用范例[J]. 北京交通大学学报, 2021, 45(2): 87-94, 110. 10.11860/j.issn.1673-0291.20200088 |
WANG N, LIU X F, JIA Y Q, et al. Algorithm design and application example of machine learning-based LTE-MR positioning technique [J]. Journal of Beijing Jiaotong University, 2021, 45(2): 87-94, 110. 10.11860/j.issn.1673-0291.20200088 | |
17 | 周志超,冯毅,夏小涵,等.基于移动蜂窝网的机器学习室外指纹定位方案[J]. 电信科学, 2021, 37(8): 85-95. 10.11959/j.issn.1000-0801.2021201 |
ZHOU Z C, FENG Y, XIA X H, et al. Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network[J]. Telecommunications Science, 2021, 37(8): 85-95. 10.11959/j.issn.1000-0801.2021201 | |
18 | TORIL M, WILLE V, LUNA-RAMÍREZ S, et al. Characterization of radio signal strength fluctuations in road scenarios for cellular vehicular network planning in LTE [J]. IEEE Access, 2021, 9: 33120-33131. 10.1109/access.2021.3060995 |
19 | MICHELI D, MURATORE G, VANNELLI A, et al. Rain effect on 4G LTE in-car electromagnetic propagation analyzed through MDT radio data measurement reported by mobile phones[J]. IEEE Transactions on Antennas and Propagation, 2021, 69(12): 8641-8651. 10.1109/tap.2021.3090505 |
20 | 中国通信标准化协会. 数字蜂窝移动通信网无线操作维护中心(OMC-R)测量报告技术要求: YD/T 2824—2015 [S]. 北京:中国标准出版社, 2015:7-8. |
China Communications Standards Association. digital cell mobile communications network OMC-R measurement report technical specification: YD/T 2824—2015 [S]. Beijing: Standards Press of China, 2015:7-8. | |
21 | 科大国创软件股份有限公司. 一种基于GIS数据进行栅格子区域划分的方法及装置: 201811625700.4[P]. 2019-05-17. |
GuoChuang Cloud Technology Company Limited. A method and equipment for dividing grid areas based on GIS data set: 201811625700.4 [P]. 2019-05-17. | |
22 | 刘大有,陈慧灵,齐红,等.时空数据挖掘研究进展[J]. 计算机研究与发展, 2013, 50(2): 225-239. 10.7544/issn1000-1239.2013.20110235 |
LIU D Y, CHEN H L, QI H, et al. Advances in spatiotemporal data mining[J]. Journal of Computer Research and Development, 2013, 50(2): 225-239. 10.7544/issn1000-1239.2013.20110235 | |
23 | 张平,陈昊.面向5G的定位技术研究综述[J]. 北京邮电大学学报, 2018, 41(5): 1-12. |
ZHANG P, CHEN H. A survey of positioning technology for 5G [J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41(5): 1-12. | |
24 | DWIVEDI S, SHREEVASTAV R, MUNIER F, et al. Positioning in 5G networks[J]. IEEE Communications Magazine, 2021, 59(11): 38-44. 10.1109/mcom.011.2100091 |
25 | KANHERE O, RAPPAPORT T S. Position location for futuristic cellular communications: 5G and beyond[J]. IEEE Communications Magazine, 2021, 59(1): 70-75. 10.1109/mcom.001.2000150 |
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