GNSS拒止环境下的伪卫星指纹定位方法

doi:10.12263/DZXB.20211167

电子学报 ›› 2022, Vol. 50 ›› Issue (4): 811-822.DOI: 10.12263/DZXB.20211167

所属专题：智能时空信息服务技术

• 智能时空信息服务技术 • 上一篇下一篇

GNSS拒止环境下的伪卫星指纹定位方法

黄璐¹^,², 蔚保国², 李宏生¹, 李隽², 贾浩男¹^,², 程建强², 李雅宁¹^,²

^1.东南大学仪器科学与工程学院微惯性仪表与先进导航技术教育部重点实验室，江苏南京 210096
^2.卫星导航系统与装备技术国家重点实验室，河北石家庄 050081

收稿日期:2021-08-28 修回日期:2022-01-17 出版日期:2022-04-25
- 作者简介:
- 黄璐男，1991年2月出生，辽宁盘锦人.2017年硕士毕业于哈尔滨工程大学信息与通信工程专业.2019年于东南大学仪器科学与工程专业攻读博士学位.中国电子学会会员.现就职于中国电科54所卫星导航系统与装备技术国家重点实验室.主要研究方向为导航、定位技术及位置服务.E-mail: hlcetc54@163.com
  蔚保国男，1966年10月出生，内蒙古凉城人.1988年毕业于国防科技大学电子工程系.博士生导师，中国电科集团首席科学家，中国电科54所副总工程师，卫星导航系统与装备技术国家重点实验室主任，中国卫星导航重大专项体系总体专家组专家，中国卫星导航定位协会室内导航定位专委会主任委员.主要研究方向为北斗卫星导航系统与综合PNT技术.
  李宏生（通讯作者）男，1964年8月出生，江苏泰州人.1995年毕业于东南大学仪器科学与工程学院.1995—1997年于华中科技大学攻读博士后.1997年进入东南大学仪器科学与工程学院任教，现为教授、博士生导师.主要研究方向为惯性仪表与惯性导航技术，微机电系统（MEMS）技术.
- 基金资助:
- 国家重点研发计划项目 (2021YFB3900800)

Pseudolite Fingerprint Positioning Method under GNSS Rejection Environment

HUANG Lu¹^,², YU Bao-guo², LI Hong-sheng¹, LI Jun², JIA Hao-nan¹^,², CHENG Jian-qiang², LI Ya-ning¹^,²

^1.Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China.
^2.State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhang, Hebei 050081, China

Received:2021-08-28 Revised:2022-01-17 Online:2022-04-25 Published:2022-04-25
- Supported by:
- Program of National Key Research and Development Program of China (2021YFB3900800)

摘要/Abstract

摘要：

伪卫星具有发射与天上卫星相同信号的能力，可以作为GNSS（Global Navigation Satellite System）信号遮挡环境下稳定可靠的定位信号源，使得基于现有终端硬件条件实现室外内连续高精度定位成为可能，因此逐渐成为室内定位领域的研究热点.本文提出了一种基于同源多通道伪卫星的指纹库匹配定位方法，利用顾及位置信息的变分自编码网络（Variational Auto-Encoder，VAE）学习伪卫星载波相位信息在隐含空间下的概率分布特征，建立伪卫星观测数据隐含特征与室内位置间的映射关系，进而实现GNSS拒止环境下的指纹匹配定位.针对指纹定位结果波动大的问题，本文提出一种粒子滤波融合处理方法，提高了定位系统的稳定性和定位精度.本文在试验环境以及机场环境下，通过大量试验验证了该定位算法在动态和静态下的定位性能，并与常用的基于指纹库匹配的定位方法进行了比较.结果表明，在室内试验环境下，动态平均定位精度为0.39 m，95%的定位误差小于0.85 m，在真实机场环境下，动态平均定位精度为0.75 m，最大定位误差为1.69 m，92%的定位误差小于1 m，验证了算法的有效性.

关键词: 伪卫星, 载波相位, 室内定位, 指纹匹配, 机器学习

Abstract:

Pseudolites have the ability to transmit the same signals as GNSS(Global Navigation Satellite System) satellites, and can provide stable and reliable positioning signals for the navigation signal obstructed environment, making it possible to achieve continuous high-precision positioning outdoors based on the existing terminal hardware conditions. Therefore, it has gradually become a research hotspot in the field of indoor positioning. In this paper, a fingerprint database matching and positioning method based on homologous multi-channel pseudolites is proposed. The variational autoencoder network that takes into account the position information is designed to learn the probability distribution characteristics of the pseudolite carrier phase information in the hidden space. Then, the mapping relationship between the hidden features of the pseudolite observation data and the indoor location is established. After this, aiming at the problem of large fluctuation of fingerprint location results, a particle filter fusion processing method is proposed to improve the stability and accuracy of the location system. In the experimental environment and airport environment, a large number of experiments verify the positioning performance of the positioning algorithm under dynamic and static conditions, and compare it with the common positioning methods based on fingerprint database matching. The results show that the dynamic average positioning accuracy is 0.39 m in the indoor test environment, and 95% of the positioning error is better than 0.85 m. In the real airport environment, the dynamic average positioning accuracy is 0.75 m, the maximum positioning error is 1.69 m, and 92% of the positioning error is better than 1m. The effectiveness of the algorithm is verified.

Key words: pseudolite, carrier phase, indoor positioning, fingerprint matching, machine learning

中图分类号:

TN967.1

黄璐, 蔚保国, 李宏生, 等. GNSS拒止环境下的伪卫星指纹定位方法[J]. 电子学报, 2022, 50(4): 811-822.

Lu HUANG, Bao-guo YU, Hong-sheng LI, et al. Pseudolite Fingerprint Positioning Method under GNSS Rejection Environment[J]. Acta Electronica Sinica, 2022, 50(4): 811-822.

图/表 17

参考文献 25

1	GANX L, HUANGL, WANGB Y, et al. Indoor positioning technology based on map information perception[J]. The Journal of Engineering, 2018, 2018(16): 1561-1566.
2	WANGJ. Pseudolite applications in positioning and navigation: Progress and problems[J]. Journal of Global Positioning Systems, 2002, 1(1): 48-56.
3	YANGC C, SHAOH R. WiFi-based indoor positioning[J]. IEEE Communications Magazine, 2015, 53(3): 150-157.
4	HERNÁNDEZN, OCAÑAM, ALONSOJ M, et al. Continuous space estimation: Increasing WiFi-based indoor localization resolution without increasing the site-survey effort[J]. Sensors(Basel, Switzerland), 2017, 17(1): 147.
5	XUH, DINGY, LIP, et al. An RFID indoor positioning algorithm based on Bayesian probability and K-nearest neighbor[J]. Sensors(Basel, Switzerland), 2017, 17(8): 1806.
6	MURATAS, YARAC, KANETAK, et al. Accurate indoor positioning system using near-ultrasonic sound from a smartphone[C]//2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies. Oxford: IEEE, 2014: 13-18.
7	PENGQ, GUANW P, WUY X, et al. Three-dimensional high-precision indoor positioning strategy using Tabu search based on visible light communication[J]. Optical Engineering, 2018, 57: 016101.
8	DE ANGELISG, MOSCHITTAA, CARBONEP. Positioning techniques in indoor environments based on stochastic modeling of UWB round-trip-time measurements[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(8): 2272-2281.
9	DE BLASIOG, QUESADA-ARENCIBIAA, GARCÍAC R, et al. Study on an indoor positioning system for harsh environments based on Wi-Fi and bluetooth low energy[J]. Sensors(Basel, Switzerland), 2017, 17(6): 1299.
10	CANTÓN PATERNAV, CALVERAS AUGÉA, PARADELLS ASPASJ, et al. A bluetooth low energy indoor positioning system with channel diversity, weighted trilateration and Kalman filtering[J]. Sensors(Basel, Switzerland), 2017, 17(12): 2927.
11	WUJ, YUZ J, JING-CHANGZ G, et al. Indoor positioning by using scanning infrared laser and ultrasonic technology[J]. Optics and Precision Engineering, 2016, 24(10): 2417-2423.
12	NILSSONJ O, RANTAKOKKOJ, HÄNDELP, et al. Accurate indoor positioning of firefighters using dual foot-mounted inertial sensors and inter-agent ranging[C]//2014 IEEE/ION Position, Location and Navigation Symposium - PLANS2014. Monterey: IEEE, 2014: 631-636.
13	XIAH, WANGX G, QIAOY Y, et al. Using multiple barometers to detect the floor location of smart phones with built-in barometric sensors for indoor positioning[J]. Sensors(Basel, Switzerland), 2015, 15(4): 7857-7877.
14	FUJIIK, SAKAMOTOY, WANGW, et al. Hyperbolic positioning with antenna arrays and multi-channel pseudolite for indoor localization[J]. Sensors(Basel, Switzerland), 2015, 15(10): 25157-25175.
15	SONGX D, FANX C, XIANGC C, et al. A novel convolutional neural network based indoor localization framework with WiFi fingerprinting[J]. IEEE Access, 2019, 7: 110698-110709.
16	WANGF, FENGJ W, ZHAOY L, et al. Joint activity recognition and indoor localization with WiFi fingerprints[J]. IEEE Access, 2019, 7: 80058-80068.
17	GUX Y, ZHUB C. An improved method of ambiguity resolution in GNSS positioning[J]. Chinese Journal of Electronics, 2019, 28(1): 215-222.
18	CAOY, ZHANGS S, YANGG Z, et al. Research on high precision tracking method of guided transport vehicle based on autonomous combination positioning[J]. Chinese Journal of Electronics, 2020, 29(4): 779-785.
19	PENGZ, GAOS, XIAOB, et al. Indoor floor plan construction through sensing data collected from smartphones[J]. IEEE Internet of Things Journal, 2018, 5(6): 4351-4364.
20	KINGMAD P, WELLINGM. Auto-encoding variational Bayes[J]. CoRR, 2013: abs/1312.6114.
21	FANGS H, LINT N, LEEK C. A novel algorithm for multipath fingerprinting in indoor WLAN environments[J]. IEEE Transactions on Wireless Communications, 2008, 7(9): 3579-3588.
22	李泽, 田增山, 王中春, 等. 基于粒子群优化的多径辅助室内定位算法[J]. 电子学报, 2020, 48(10): 1952-1960.
	LIZ, TIANZ S, WANGZ C, et al. Multipath-assisted indoor localization algorithm based on particle swarm optimization[J]. Acta Electronica Sinica, 2020, 48(10): 1952-1960. (in Chinese)
23	TANGH L, XUEF, LIUT, et al. Indoor positioning algorithm fusing multi-source information[J]. Wireless Personal Communications, 2019, 109(4): 2541-2560.
24	XIAOL, BEHBOODIA, MATHARR. Learning the Localization Function: Machine Learning Approach to Fingerprinting Localization[EB/OL]. (2018)[2021]. .
25	KIMK S, WANGR, ZHONGZ, et al. Large-scale location-aware services in access: Hierarchical building/floor classification and location estimation using Wi-Fi fingerprinting based on deep neural networks[C]//2017 International Workshop on Fiber Optics in Access Network (FOAN). Munich: IEEE, 2017: 1-5.

方法	MNIST	CIFAR10	REUTERS	BLE RSSI	UJIndoorLoc
GMM	55.37(±0.08)	55.46(±0.10)	56.01(±0.11)	45.56(±0.12)	50.23(±0.12)
AE+GMM	84.56(±0.11)	73.59(±0.08)	71.18(±0.11)	74.61(±0.11)	85.91(±0.11)
VaDE^[27]	94.46(±0.10)	88.36(±0.05)	79.58(±0.10)	89.34(±0.04)	91.85(±0.04)
Our Model	97.77(±0.08)	90.11(±0.04)	82.07(±0.08)	91.36(±0.05)	92.96(±0.02)

方法	MNIST	CIFAR10	REUTERS	BLE RSSI	UJIndoorLoc
GMM	55.37(±0.08)	55.46(±0.10)	56.01(±0.11)	45.56(±0.12)	50.23(±0.12)
AE+GMM	84.56(±0.11)	73.59(±0.08)	71.18(±0.11)	74.61(±0.11)	85.91(±0.11)
VaDE^[27]	94.46(±0.10)	88.36(±0.05)	79.58(±0.10)	89.34(±0.04)	91.85(±0.04)
Our Model	97.77(±0.08)	90.11(±0.04)	82.07(±0.08)	91.36(±0.05)	92.96(±0.02)

算法	KNN	SVM	Xiao L	Kim K S	Ours Model
平均误差/m	2.03	1.56	1.03	0.79	0.39
95%误差/m	3.77	3.76	2.25	1.43	0.85

算法	KNN	SVM	Xiao L	Kim K S	Ours Model
平均误差/m	2.03	1.56	1.03	0.79	0.39
95%误差/m	3.77	3.76	2.25	1.43	0.85

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GNSS拒止环境下的伪卫星指纹定位方法

Pseudolite Fingerprint Positioning Method under GNSS Rejection Environment

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