面向同频干扰环境的5G机会信号定位算法研究

孙骞; 丁天语; 简鑫; 李一兵; 于飞

doi:10.11999/JEIT231423

面向同频干扰环境的5G机会信号定位算法研究

doi: 10.11999/JEIT231423

孙骞^{1, 2, ,},
丁天语^{1, 2},
简鑫^{1, 2},
李一兵^{1, 2},
于飞³

1.
哈尔滨工程大学信息与通信工程学院哈尔滨 150001
2.
哈尔滨工程大学先进船舶通信与信息技术重点实验室哈尔滨 150001
3.
哈尔滨工程大学数学科学学院哈尔滨 150001

基金项目: 国家自然科学基金(52271311)

详细信息

作者简介:
孙骞：男，教授，研究方向为通导一体化技术

丁天语：男，硕士生，研究方向为机会信号导航技术

简鑫：男，硕士生，研究方向为机会信号导航技术

李一兵：男，教授，研究方向为通导一体化技术

于飞：男，教授，研究方向为机会信号导航技术

通讯作者:
孙骞　qsun@hrbeu.edu.cn

中图分类号: TN911.7; TN96
计量
- 文章访问数: 84
- HTML全文浏览量: 63
- PDF下载量: 20
- 被引次数: 0
出版历程
- 收稿日期: 2023-12-26
- 修回日期: 2024-03-15
- 网络出版日期: 2024-03-27

Research on Opportunistic Localization with 5G Signals in Co-channel Interference Environments

SUN Qian^{1, 2
, ,},
DING Tianyu^{1, 2},
JIAN Xin^{1, 2},
LI Yibing^{1, 2},
YU Fei³

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin 150001, China
3.
College of Mathematical Sciences, Harbin Engineering University, Harbin 150001, China

Funds: The National Natural Science Foundation of China (52271311)

摘要

摘要: 针对全球导航卫星系统(GNSS)拒止环境下定位精度难以保证的问题，该文设计了一种基于新无线电(NR)机会信号的定位方案，并提出一种基于干扰消除子空间追踪(ICSP)算法，解决超密集网络(UDNs)和异构网络(HetNets)环境中同频干扰对定位观测量提取精度不足的问题。通过仿真实验和通用软件无线电外设(USRP)半实物仿真，验证了ICSP算法在复杂网络环境中优化5G机会信号接收机性能、提高定位精度上的有效性。
- 机会信号导航 /
- 新无线电定位技术 /
- 干扰消除子空间追踪 /
- 同频干扰 /
- 软件无线电平台
Abstract: In response to the challenge of ensuring positioning accuracy in environments where the Global Navigation Satellite System (GNSS) is denied, a positioning scheme based on opportunistic New Radio (NR) signals is devised and an Interference Cancellation Subspace Pursuit (ICSP) algorithm is proposed in this paper. This algorithm aims to resolve the issue of inadequate precision in the extraction of positioning observations due to co-channel interference within Ultra-Dense Networks (UDNs) and Heterogeneous Networks (HetNets). The effectiveness of the ICSP algorithm in optimizing the performance of 5G opportunistic signal receivers and enhancing positioning accuracy in complex network environments has been validated through simulation experiments and semi-physical simulations utilizing the Universal Software Radio Peripheral (USRP).
- Navigation via signals of opportunity /
- New Radio(NR) positioning technology /
- Interference Cancellation Subspace Pursuit(ICSP) /
- Co-channel Interference /
- Software defined radio platform

HTML全文

图 1 追踪环路原理框图

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图 2 场景1恢复能力评估

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图 3 场景2恢复能力评估

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图 4 实验设备及环境

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图 5 实地基站图

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图 6 实测定位覆盖率

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图 7 伪距测量值图

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图 8 定位结果图

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表 1 算法复杂度

算法	所需乘法次数
SIC	$ KCNL{\text{ + }}K{N^2} $
SCP	$ KCNL{\text{ + }}N({K^3}{L^2} + {K^2}L) + O({L^3}) $
ICSP	$ KCNL{\text{ + }}N({K^3}{L^2} + {K^2}L{\text{ + }}N){\text{ + }}O({L^3}) $

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表 2 基站基本参数

基站ID号	纬度(°)	经度(°)	高度(m)	中心频率(MHz)	运营商
418	45.769 187 53	126.674 699 26	181.371	3 509.76	中国联通
348	45.771 155 50	126.678 329 04	152.963	3 509.76	中国联通
47	45.771 806 62	126.674 012 97	150.758	3 509.76	中国联通
11	45.770 464 10	126.676 657 18	132.231	3 509.76	虚拟基站

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表 3 实验接收参数

参数名称	参数值
USRP接收带宽	10 ${\text{MHz}}$
采样率	30.72 $ {\text{MSps}} $
晶振类型	温补晶振(Temperature Compensated Crystal Oscillator, TCXO)
同步精度	30 $ {\text{ns}} $
中心频率	3509.76 $ {\text{MHz}} $

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参考文献(15)

[1]	Defense Advanced Research Projects Agency (DARPA). Adaptable navigation systems[EB/OL]. https://www.darpa.mil/program/adaptable-navigation-systems, 2023.
[2]	EL-SHEIMY N and LI You. Indoor navigation: State of the art and future trends[J]. Satellite Navigation, 2021, 2(1): 7. doi: 10.1186/S43020-021-00041-3.
[3]	OBEIDAT H, SHUAIEB W, OBEIDAT O, et al. A review of indoor localization techniques and wireless technologies[J]. Wireless Personal Communications, 2021, 119(1): 289–327. doi: 10.1007/s11277-021-08209-5.
[4]	SHARMA N and KUMAR K. Resource allocation trends for ultra dense networks in 5G and beyond networks: A classification and comprehensive survey[J]. Physical Communication, 2021, 48: 101415. doi: 10.1016/j.phycom.2021.101415.
[5]	STOYNOV V, POULKOV V, VALKOVA-JARVIS Z, et al. Ultra-dense networks: Taxonomy and key performance indicators[J]. Symmetry, 2023, 15(1): 2. doi: 10.3390/sym15010002.
[6]	RAJAMOHAN N and KANNU A P. Downlink synchronization techniques for heterogeneous cellular networks[J]. IEEE Transactions on Communications, 2015, 63(11): 4448–4460. doi: 10.1109/TCOMM.2015.2474381.
[7]	GOWDA N M and KANNU A P. Compressive sensing framework for signal processing in heterogeneous cellular networks[C]. 2012 IEEE Global Communications Conference, Anaheim, USA, 2012: 3610–3615. doi: 10.1109/GLOCOM.2012.6503676.
[8]	王旭东, 刘帅, 吴楠. CAEFi: 基于卷积自编码器降维的信道状态信息指纹室内定位方法[J]. 电子与信息学报, 2022, 44(8): 2757–2766. doi: 10.11999/JEIT210663. WANG Xudong, LIU Shuai, and WU Nan. CAEFI: Channel state information fingerprint indoor location method using convolutional autoencoder for dimension reduction[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2757–2766. doi: 10.11999/JEIT210663.
[9]	刘凯凯, 田增山, 李泽, 等. 仓储场景中基于无线标签的三维定位方法[J]. 电子与信息学报, 2023, 45(12): 4218–4227. doi: 10.11999/JEIT221269. LIU Kaikai, TIAN Zengshan, LI Ze, et al. 3-D localization method based on wireless tags in warehouse scenarios[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4218–4227. doi: 10.11999/JEIT221269.
[10]	JIANG Feng, ZHANG Zhenkai, and NAJAFABADI H E. Deep sea TDOA localization method based on improved OMP algorithm[J]. IEEE Access, 2019, 7: 168151–168161. doi: 10.1109/ACCESS.2019.2954330.
[11]	SHAMAEI K and KASSAS Z M. Receiver design and time of arrival estimation for opportunistic localization with 5G signals[J]. IEEE Transactions on Wireless Communications, 2021, 20(7): 4716–4731. doi: 10.1109/TWC.2021.3061985.
[12]	CHEN Liang, ZHOU Xin, CHEN Feifei, et al. Carrier phase ranging for indoor positioning with 5G NR signals[J]. IEEE Internet of Things Journal, 2021, 9(13): 10908–10919. doi: 10.1109/JIOT.2021.3125373.
[13]	GOWDA N M and KANNU A P. Interferer identification in hetnets using compressive sensing framework[J]. IEEE Transactions on Communications, 2013, 61(11): 4780–4787. doi: 10.1109/TCOMM.2013.092813.130196.
[14]	RAJAMOHAN N, JOSHI A, and KANNU A P. Joint block sparse signal recovery problem and applications in LTE cell search[J]. IEEE Transactions on Vehicular Technology, 2017, 66(2): 1130–1143. doi: 10.1109/TVT.2016.2552247.
[15]	SHAMAEI K. Exploiting cellular signals for navigation: 4G to 5G[D]. [Ph. D. dissertation], University of California, 2020.