基于CSI的轻量级自适应井下定位算法

doi:10.6040/j.issn.1672-3961.0.2018.356

摘要/Abstract

摘要：

针对传统井下定位成本高、工作危险系数大的问题,提出一种基于信道状态信息(channel state information, CSI)的轻量级自适应井下定位(lightweight self-adaptive underground positioning algorithm, LSA)方法。LSA方法以细粒度的CSI替代粗粒度的接收信号强度(received signal strength indicator, RSSI)来获得更高的定位精度,采用逆傅里叶变换将原始CSI数据转换为信道脉冲响应,以此选取视距信号,并通过构建CSI视距信号衰减模型实现轻量级的精确测距;基于井下现有WiFi网络中的访问接入点(access points, APs)位置和井下巷道特征,计算目标相对AP的方向,根据方向和测距结果完成定位。该方法能够自适应于AP在巷道中的任意位置部署,并利用拐角识别优化算法进一步提高定位的精度。试验结果表明,该方法能够使得定位中位数误差达到0.53 m,且无需在井下单独部署任何定位系统,性能明显优于已提出的CDPF、FILA等其他定位算法。

关键词: 信道状态信息, 信号衰减模型, 井下定位

Abstract:

To solve the problem of high cost and working hazard factor of traditional downhole positioning methods, a lightweight self-adaptive CSI-based positioning algorithm in underground mine was proposed. The fine-grained CSI was used to obtain higher positioning accuracy rather than coarse-grained RSSI, inverse fast Fourier transform was adopted to transform CSI data to channel impulse response so as to get the line-of-sight signal, an attenuation model of line-of-sight signal of CSI was built to implement accurate ranging, position features of existing point access points (APs) in wireless fidelity and characteristics of rock roadways was utilized to calculate orientation of target relative to AP, which finally completed location according to orientation and distance. LSA was adaptive to arbitrary deployment modes, and the corner recognition optimization algorithm was used to improve positioning accuracy. The experimental results showed that LSA method median error could reach 0.53 m and eliminate the need to deploy any positioning system in the well alone, the performance was superrior to CDPF and FILA.

Key words: channel state information, signal attenuation model, underground positioning

中图分类号:

TP391

岳俊梅,张冬梅. 基于CSI的轻量级自适应井下定位算法[J]. 山东大学学报 (工学版), 2019, 49(5): 112-118.

Junmei YUE,Dongmei ZHANG. Lightweight self-adaptive CSI-based positioning algorithm in underground mine[J]. Journal of Shandong University(Engineering Science), 2019, 49(5): 112-118.

图/表 9

图1

图2

图3

图4

图5

图6

图7

图8

图9

参考文献 25

1	SALAZAR A S, AGUILAR L, LICEA G. Estimating indoor zone-level location using Wi-Fi RSSI fingerprinting based on fuzzy inference system[C]//International Conference on Mechatronics, Electronics and Automotive Engineering. Morelos, Mexico: IEEE, 2013: 178-184.
2	ZAFARI F, PAPAPANAGIOTOU I, HACKER T J. A novel Bayesian filtering based algorithm for RSSI-based indoor localization[C]//IEEE International Conference on Communications. Kansas City, USA: IEEE, 2018: 79-84.
3	SHUE S, CONRAD J M. Reducing the effect of signal multipath fading in RSSI-distance estimation using Kalman filters[C]//Communications & Networking Symposium. San Diego, USA: Society for Computer Simulation International, 2016: 5.
4	XUE W , HUA X , LI Q , et al. A new weighted algorithm based on the uneven spatial resolution of RSSI for indoor localization[J]. IEEE Access, 2018, 6 (99): 26588- 26595.
5	曾碧, 毛勤. 改进的室内三维模糊位置指纹定位算法[J]. 山东大学学报(工学版), 2015, 45 (3): 22- 27.
	ZENG Bi , MAO Qin . Improved indoor 3-D fuzzy position fingerprint localization algorithm[J]. Journal of Shandong University (Engineering Science), 2015, 45 (3): 22- 27.
6	IEEE. Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: policies and procedures for operation in the TV bands: ANSI/IEEE Std 802.11v[S]. New York, USA: LAN/MAN Standards Committee of the IEEE Computer Society Std., 2011.
7	HALPERIN D , HU W , SHETH A , et al. Tool release: gathering 802.11n traces with channel state information[J]. Acm Sigcomm Computer Communication Review, 2011, 41 (1): 53- 53. doi: 10.1145/1925861.1925870
8	LIU Y , DRAPER S C , SAYEED A M . A secret key generation system based on multipath channel randomness: RSSI vs CSSI[J]. Information Forensics & Security IEEE Transactions on, 2011, (5): 1484- 1497.
9	SHI S , SIGG S , CHEN L , et al. Accurate location tracking from CSI-based passive device-free probabilistic fingerprinting[J]. IEEE Transactions on Vehicular Technology, 2018, 67 (6): 5217- 5230. doi: 10.1109/TVT.2018.2810307
10	LIAO L , ZAKHAROV Y , MITCHELL P D . Underwater localization based on grid computation and its application to transmit beamforming in multiuser uwa communications[J]. IEEE Access, 2018, (6): 4297- 4307.
11	WANG X , GAO L , MAO S , et al. CSI-based fingerprinting for indoor localization: a deep learning approach[J]. IEEE Transactions on Vehicular Technology, 2017, 66 (1): 763- 776.
12	WU K , XIAO J , YI Y , et al. FILA: fine-grained indoor localization[J]. Proceedings-IEEE INFOCOM, 2012, 131 (5): 2210- 2218.
13	WU K , XIAO J , YI Y , et al. CSI-based indoor localization[J]. IEEE Transactions on Parallel & Distributed Systems, 2013, 24 (7): 1300- 1309.
14	SONG Q , GUO S , LIU X , et al. CSI amplitude fingerprinting-based NB-IoT indoor localization[J]. IEEE Internet of Things Journal, 2018, 5 (3): 1494- 1504. doi: 10.1109/JIOT.2017.2782479
15	SEN S, CHOUDHURY R R, MINKA T. You are facing the Mona Lisa: spot localization using PHY layer information[C]//International Conference on Mobile Systems, Applications, and Services. New York, USA: ACM, 2012: 183-196.
16	张一衡, 崔琪楣, 陶小峰. 多用户MIMO-OFDM系统低速率CSI反馈方法及信道容量分析[J]. 电子与信息学报, 2009, 31 (9): 2188- 2192.
	ZHANG Yiheng , CUI Qimei , TAO Xiaofeng . Low rate CSI feedback and capacity analysis in multiuser-MIMO-OFDM System[J]. Journal of Electronics & Information Technology, 2009, 31 (9): 2188- 2192.
17	胡楚锋, 郭淑霞, 李南京, 等. 超视距宽带信号同步测量技术研究[J]. 仪器仪表学报, 2014, (11): 2531- 2537.
	HU Chufeng , GUO Shuxia , LI Nanjing , et al. Synchronous measurement for a wideband signal at non-line-of-sight[J]. Chinese Journal of Scientific Instrument, 2014, (11): 2531- 2537.
18	WANG X, WANG X, MAO S. ResLoc: deep residual sharing learning for indoor localization with CSI tensors[C]//International Symposium on Personal, Indoor, and Mobile Radio Communications. Montreal, Canada: IEEE, 2018.
19	龙保任, 王峰, 利传迈, 等. 基于聚类的组合时间反转算法的CSI指纹室内定位研究[J]. 电视技术, 2018, 42 (11): 58- 63. doi: 10.3969/j.issn.1671-8658.2018.11.015
	LONG Baoren , WANG Feng , LI Chuanmai , et al. Research on CSI fingerprint indoor positioning based on clustering-based combined time reversal algorithm[J]. Video Engineering, 2018, 42 (11): 58- 63. doi: 10.3969/j.issn.1671-8658.2018.11.015
20	WANG X , GAO L , MAO S . CSI phase fingerprinting for indoor localization with a deep learning approach[J]. IEEE Internet of Things Journal, 2017, 3 (6): 1113- 1123.
21	CHAPRE Y, IGNJATOVIC A, SENEVIRATNE A, et al. CSI-MIMO: Indoor Wi-Fi fingerprinting system[C]//Local Computer Networks. Edmonton, Canada: IEEE, 2014: 202-209.
22	HALPERIN D, HU W, SHETH A, et al. Predictable 802.11 packet delivery from wireless channel measurements[C]//ACM SIGCOMM 2010 Conference. New Delhi, India: ACM, 2010: 159-170.
23	马德鹏, 杨永杰, 曹吉胜, 等. 基于能量释放的深井巷道断面形状优化[J]. 中南大学学报(自然科学版), 2015, 46 (9): 3354- 3360.
	MA Depeng , YANG Yongjie , CAO Jisheng , et al. Optimization design of cross section shape of deep roadways based on characteristics of energy release[J]. Journal of Central South University(Science and Technology), 2015, 46 (9): 3354- 3360.
24	RAPPAPORT T S . Wireless communications: principles and practice[M]. 2nd ed Upper Saddle River, USA: Prentice Hall, 2001.
25	WANG Y, ZHOU Z, WU K. Sensor-free corner shape detection by wireless networks[C]//IEEE International Conference on Parallel and Distributed Systems. Taiwan, China: IEEE, 2015: 306-312.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

[1]	邓彬, 张宗包, 赵文猛, 罗新航, 吴秋伟. 基于云边协同和图神经网络的电动汽车充电站负荷预测方法[J]. 山东大学学报 (工学版), 2025, 55(5): 62-69.
[2]	李二超, 张智钊. 在线动态订单需求车辆路径规划[J]. 山东大学学报 (工学版), 2024, 54(5): 62-73.
[3]	杨巨成, 魏峰, 林亮, 贾庆祥, 刘建征. 驾驶员疲劳驾驶检测研究综述[J]. 山东大学学报 (工学版), 2024, 54(2): 1-12.
[4]	肖伟, 郑更生, 陈钰佳. 结合自训练模型的命名实体识别方法[J]. 山东大学学报 (工学版), 2024, 54(2): 96-102.
[5]	胡钢, 王乐萌, 卢志宇, 王琴, 徐翔. 基于节点多阶邻居递阶关联贡献度的重要性辨识[J]. 山东大学学报 (工学版), 2024, 54(1): 1-10.
[6]	李家春,李博文,常建波. 一种高效且轻量的RGB单帧人脸反欺诈模型[J]. 山东大学学报 (工学版), 2023, 53(6): 1-7.
[7]	樊禹江,黄欢欢,丁佳雄,廖凯,余滨杉. 基于云模型的老旧小区韧性评价体系[J]. 山东大学学报 (工学版), 2023, 53(5): 1-9, 19.
[8]	李颖,王建坤. 基于监督图正则化和信息融合的轻度认知障碍分类方法[J]. 山东大学学报 (工学版), 2023, 53(4): 65-73.
[9]	余明骏,刁红军,凌兴宏. 基于轨迹掩膜的在线多目标跟踪方法[J]. 山东大学学报 (工学版), 2023, 53(2): 61-69.
[10]	刘行,杨璐,郝凡昌. 基于多特征融合的手指静脉图像检索方法[J]. 山东大学学报 (工学版), 2023, 53(2): 118-126.
[11]	刘方旭,王建,魏本征. 基于多空间注意力的小儿肺炎辅助诊断算法[J]. 山东大学学报 (工学版), 2023, 53(2): 135-142.
[12]	于艺旋,杨耕,耿华. 连续复合运动的多模态层次化关键帧提取方法[J]. 山东大学学报 (工学版), 2023, 53(2): 42-50.
[13]	黄华娟,程前,韦修喜,于楚楚. 融合Jaya高斯变异的自适应乌鸦搜索算法[J]. 山东大学学报 (工学版), 2023, 53(2): 11-22.
[14]	张豪,李子凌,刘通,张大伟,陶建华. 融合社会学因素的模糊贝叶斯网技术预测模型[J]. 山东大学学报 (工学版), 2023, 53(2): 23-33.
[15]	吴艳丽,刘淑薇,何东晓,王晓宝,金弟. 刻画多种潜在关系的泊松-伽马主题模型[J]. 山东大学学报 (工学版), 2023, 53(2): 51-60.