Improved adaptively robust estimation algorithm for GNSS spoofer considering continuous observation error

doi:10.23919/JSEE.2022.000118

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (5): 1237-1248.doi: 10.23919/JSEE.2022.000118

• CONTROL THEORY AND APPLICATION • Previous Articles Next Articles

Improved adaptively robust estimation algorithm for GNSS spoofer considering continuous observation error

Yangjun GAO^1,²(), Guangyun LI^1,*(), Zhiwei LYU¹(), Lundong ZHANG¹(), Zhongpan LI³()

¹ Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
² State Key Laboratory of Geo-Information Engineering, Xi’an 710000, China
³ Unit 96944 of the PLA, Beijing 100000, China

Received:2021-05-04 Online:2022-10-27 Published:2022-10-27
Contact: Guangyun LI E-mail:951242669@qq.com;guangyun_li_chxy@163.com;lvzhiwei@sina.com;zhangldxd@163.com;724136242@qq.com
About author:|GAO Yangjun was born in 1995. He received his B.S. and M.S. degrees from PLA Strategic Support Force Information Engineering University, Zhengzhou, China, in 2017 and 2020, respectively. He is a Ph.D. candidate in the Institute of Geospatial Information at the PLA Strategic Support Force Information Engineering University. His research interests include global navigation satellite system applications. E-mail: 951242669@qq.com||LI Guangyun was born in 1965. He received his M.S. degree from PLA Institute of Surveying and Mapping, China, in 1987. Currently, he is a professor of the Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University. His research interests include navigation and location services and applications. E-mail: guangyun_li_chxy@163.com||LYU Zhiwei was born in 1974. He obtained his Ph.D. degree in space geodetic survey and navigation from PLA Strategic Support Information Engineering University, China, in 2010. He is currently a professor at PLA Strategic Support Information Engineering University, China. His current research focuses mainly on satellite navigation data processing and space geodetic survey. E-mail: lvzhiwei@sina.com||ZHANG Lundong was born in 1980. He received his Ph.D. degree from National University of Defense Technology, China, in 2012. He is currently a lecturer at PLA Strategic Support Information Engineering University, China. His current research focuses mainly on indoor navigation and pedestrian. E-mail: zhangldxd@163.com||LI Zhongpan was born in 1994. He received his B.S. and M.S. degrees from PLA Strategic Support Force Information Engineering University, Zhengzhou, China, in 2017 and 2019, respectively. His research interests include global navigation satellite system applications. E-mail: 724136242@qq.com
Supported by:
This work was supported by the State Key Laboratory of Geo-Information Engineering (SKLGIE2022-Z-2-1), and the National Natural Science Foundation of China (41674024;42174036)

Abstract

Abstract:

Once the spoofer has controlled the navigation system of unmanned aerial vehicle (UAV), it is hard to effectively control the error convergence to meet the threshold condition only by adjusting parameters of estimation if estimation of the spoofer on UAV has continuous observation error. Aiming at this problem, the influence of the spoofer’s state estimation error on spoofing effect and error convergence conditions is theoretically analyzed, and an improved adaptively robust estimation algorithm suitable for steady-state linear quadratic estimator is proposed. It enables the spoofer’s estimator to reliably estimate UAV status in real time, improves the robustness of the estimator in responding to observation errors, and accelerates the convergence time of error control. Simulation experiments show that the mean value of normalized innovation squared (NIS) is reduced by 88.5%, and the convergence time of NIS value is reduced by 76.3%, the convergence time of true trajectory error of UAV is reduced by 42.3%, the convergence time of estimated trajectory error of UAV is reduced by 67.4%, the convergence time of estimated trajectory error of the spoofer is reduced by 33.7%, and the convergence time of broadcast trajectory error of the spoofer is reduced by 54.8% when the improved algorithm is used. The improved algorithm can make UAV deviate from preset trajectory to spoofing trajectory more effectively and more subtly.

Key words: spoofing, unmanned aerial vehicle (UAV), spoofer, adaptively robust estimation, global navigation satellite system (GNSS), normalized innovation squared (NIS)

Yangjun GAO, Guangyun LI, Zhiwei LYU, Lundong ZHANG, Zhongpan LI. Improved adaptively robust estimation algorithm for GNSS spoofer considering continuous observation error[J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1237-1248.

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Parameter	${\sigma _x}$ /m	${\sigma _v}$ /(m/s)	${\sigma _{xv}}$	${\sigma _a}$ / $({\text{m/} }{ {\text{s} }^{\text{2} } })$	${\sigma _b}$ / $({\text{m/} }{ {\text{s} }^{\text{2} } })$
Unit value	2	0.3	0	${\text{0}}{\text{.05}}$	${\text{1} }{\text{.24} } \times {\text{1} }{ {\text{0} }^{ {{ - 5} } } }$

Parameter	${\bar \sigma _x}$ /m	${\bar \sigma _v}$ /(m/s)	${\bar \sigma _{xv}}$	${\bar \sigma _a}$ / $({\text{m/} }{ {\text{s} }^{\text{2} } })$
Unit value	2	0.3	0	${\text{0}}{\text{.5}}$

Types of convergence time	Type of experiment	X axis convergence time/s	Y axis convergence time/s	Z axis convergence time/s	Threshold of convergence/m
1	1	234	388	2377	1
1	2	115	151	2022	1
2	1	172	257	1178	0.1
2	2	58	27	631	0.1
3	1	45	41	1345	1
3	2	32	29	770	1
4	1	209	284	1609	0.1
4	2	84	84	1061	0.1

Types of convergence time	X		Y		Z		Average reduction in percentage/s
Types of convergence time	Shortenen time/s	Percentage of shorten time/%	Shortenen time/s	Percentage of shorten time/%	Shortenen time/s	Percentage of shorten time/%	Average reduction in percentage/s
1	119	50.9	237	61.1	355	14.9	42.3
2	114	66.3	230	89.5	547	46.4	67.4
3	13	28.9	12	29.3	575	42.8	33.7
4	125	59.8	200	70.4	548	34.1	54.8

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Improved adaptively robust estimation algorithm for GNSS spoofer considering continuous observation error

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