Impact of ionospheric irregularity on SBAS integrity: spatial threat modeling and improvement

doi:10.21629/JSEE.2018.05.03

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (5): 908-917.doi: 10.21629/JSEE.2018.05.03

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Impact of ionospheric irregularity on SBAS integrity: spatial threat modeling and improvement

Junjie BAO(), Rui LI(), Pan LIU*(), Zhigang HUANG()

School of Electronic and Information Engineering, Beihang University, Beijing 100191, China

Received:2017-04-26 Online:2018-10-26 Published:2018-11-14
Contact: Pan LIU E-mail:baojunjie@buaa.edu.cn;ruin@263.net;liupanlzu@yeah.net;baahzg@163.com
About author:BAO Junjie was born in 1989. She is a Ph.D. candidate in communications and information systems at School of Electronic and Information Engineering, Beihang University. Her current research is targeted on the technology of satellite-based augmentation system integrity algorithms, mainly ionospheric correction. E-mail: baojunjie@buaa.edu.cn|LI Rui was born in 1976. He received his Ph.D. degree from Beihang University in 2006. He is now a senior engineer in the School of Electronic and Information Engineering, Beihang University. His main interests cover required navigation performance and global navigation satellite system augmentation technologies in satellite-based augmentation system, ground based augmentation system, and aircraft based augmentation system. E-mail: lee ruin@263.net|LIU Pan was born in 1993. She received her M.S. degree from Beihang University in 2017. Her research interest focuses on the technology of ionospheric correction. E-mail: liupanlzu@yeah.net|HUANG Zhigang was born in 1962. He received his Ph.D. degree from Beihang University in 2004. He is currently a professor in the School of Electronics and Information Engineering, Beihang University. His research interests include integrity algorithms and related software for ground based augmentation system and satellite-based augmentation system. E-mail: baahzg@163.com
Supported by:
the National Natural Science Foundation of China(41304024);This work was supported by the National Natural Science Foundation of China (41304024)

Abstract

Abstract:

The ionosphere, as the largest and least predictable error source, its behavior cannot be observed at all places simultaneously. The confidence bound, called the grid ionospheric vertical error (GIVE), can only be determined with the aid of a threat model which is used to restrict the expected ionospheric behavior. However, the spatial threat model at present widespread used, which is based on fit radius and relative centroid metric (RCM), is too conservative or the resulting GIVEs will be too large and will reduce the availability of satellite-based augmentation system (SBAS). In this paper, layered two-dimensional parameters, the vertical direction double RCMs, are introduced based on the spatial variability of the ionosphere. Comparing with the traditional threat model, the experimental results show that the user ionospheric vertical error (UIVE) average reduction rate reaches 16%. And the 95% protection level of conterminous United States (CONUS) is 28%, even under disturbed days, which reaches about 5% reduction rates. The results show that the system service performance has been improved better.

Key words: ionospheric delay, spatial threat model, relative centroid metric (RCM), user ionospheric vertical error (UIVE)

Junjie BAO, Rui LI, Pan LIU, Zhigang HUANG. Impact of ionospheric irregularity on SBAS integrity: spatial threat modeling and improvement[J]. Journal of Systems Engineering and Electronics, 2018, 29(5): 908-917.

Figures/Tables 13

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References 30

1	KIM J, LEE Y J. Using ionospheric corrections from the space-based augmentation systems for low earth orbiting satellites. GPS Solutions, 2015, 19 (3): 423- 431. doi: 10.1007/s10291-014-0402-8
2	RATNAM D V, DABBAKUTI J R K K, SUNDA S. Modeling of ionospheric time delays based on a multishell spherical harmonics function approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10 (12): 5784- 5790. doi: 10.1109/JSTARS.2017.2743695
3	SIVAVARAPRASAD G, RATNAM D V. Short-term forecasting of ionospheric total electron content over a low-latitude global navigation satellite system station. IET Radar Sonar & Navigation, 2017, 11 (8): 1309- 1320.
4	TAKAHASHI H, WRASSE C M, DENARDINI C M, et al. Ionospheric TEC weather map over South America. Space Weather-the International Journal of Research & Applications, 2017, 14 (11): 937- 949.
5	GAMPALA S, DEVANABOYINA V R. Application of SST to forecast ionospheric delays using GPS observations. IET Radar Sonar & Navigation, 2017, 11 (7): 1070- 1080.
6	SPARKS L, KOMJATHY A, MANNUCCI A J. Sudden ionospheric delay decorrelation and its impact on the wide area augmentation system (WAAS). Radio Science, 2016, 39 (1): 1- 8. doi: 10.17946/JRST.2016.39.1.01
7	RODRÍGUEZ-BOUZA M, PAPARINI C, OTERO X, et al. Southern European ionospheric TEC maps based on Kriging technique to monitor ionosphere behavior. Advances in Space Research, 2017, 60 (8): 1606- 1616. doi: 10.1016/j.asr.2017.05.008
8	HUANG L, ZHANG H, XU P, et al. Kriging with unknown variance components for regional ionospheric reconstruction. Sensors, 2016, 17 (3): 468- 500.
9	LAWRENCE S, BLANCH J, PANDYA N. Estimating ionospheric delay using Kriging: 1. Methodology. Radio Science, 2011, 46 (6): 1- 13.
10	ABDELAZEEM M, ÇELIK R N, EL-RABBANY A. An accurate Kriging-based regional ionospheric model using combined GPS/BeiDou observations. Journal of Applied Geodesy, 2018, 12 (1): 65- 76. doi: 10.1515/jag-2017-0023
11	LIU D, YU X, CHEN L, et al. Analysis on ionospheric delay variogram modeling in China. Proc. of the China Satellite Navigation Conference, 2017: 119-130.
12	LEE J, KIM M. Optimized GNSS station selection to support long-term monitoring of ionospheric anomalies for aircraft landing systems. IEEE Trans. on Aerospace & Electronic Systems, 2017, 53 (1): 236- 246.
13	GRUNWALD G, BAKUŁA M, CIEĆKO A, et al. Examination of GPS/EGNOS integrity in north-eastern Poland. IET Radar Sonar & Navigation, 2016, 10 (1): 114- 121.
14	HAMEL P, SAMBOU D C, DARCES M, et al. Kriging method to perform scintillation maps based on measurement and GISM model. Radio Science, 2016, 49 (9): 746- 752.
15	LIU P, LI R. Improving extended Kriging with chapman model and exponential variation function model. Lecture Notes in Electrical Engineering, 2016, v389, 177- 187.
16	MINKWITZ D, VAN D B K G, GERZEN T, et al. Tomography of the ionospheric electron density with geostatistical inversion. Annales Geophysicae, 2015, 33 (8): 1071- 1079. doi: 10.5194/angeo-33-1071-2015
17	MINKWITZ D, DEN BOOGAART K G, GERZEN T, et al. Ionospheric tomography by gradient-enhanced Kriging with STEC measurements and ionosonde characteristics. Annales Geophysicae, 2016, 34 (11): 999- 1010. doi: 10.5194/angeo-34-999-2016
18	EUGENE B. Considerations on ionospheric correction and integrity algorithm for Korean SBAS. Journal of Positioning, Navigation, and Timing, 2014, 3 (1): 17- 23. doi: 10.11003/JPNT.2014.3.1.017
19	AMMANA S R, ACHANTA S D. Estimation of overbound on ionospheric spatial decorrelation over low-latitude region for ground-based augmentation systems. IET Radar Sonar & Navigation, 2016, 10 (3): 637- 645.
20	BORRIES C, JAKOWSKI N, KAURISTIE K, et al. On the dynamics of large-scale traveling ionospheric disturbances over Europe on 20 November 2003. Journal of Geophysical Research Space Physics, 2017, 122 (1): 1199- 1211. doi: 10.1002/2016JA023050
21	JUAN B, TODD W, PER E. Ionospheric threat model methodology for WAAS. Navigation, 2002, 49 (49): 103- 107.
22	SAKAI T, MATSUNAGA K, HOSHINOO K, et al. Modeling ionospheric spatial threat based on dense observation datasets for MSAS. Proc. of the 21st International Technical Meeting of the Satellite Division of The Institute of Navigation, 2008: 1918-1928.
23	ONUR K M, YEGANEHSAHAB A, DURMAZ M. Estimation of GBAS ionospheric threat model parameters using continuous GNSS observations for category i operations. Proc. of the EGU General Assembly Conference Abstracts. 2017: 13642.
24	LEE J, PULLEN S, DATTA-BARUA S, et al. Real-time ionospheric threat adaptation using a space weather prediction for GNSS-based aircraft landing systems. IEEE Trans. on Intelligent Transportation Systems, 2017, 18 (7): 1752- 1761. doi: 10.1109/TITS.2016.2627600
25	BANG E, LEE J, LEE J, et al. Constructing ionospheric irregularity threat model for Korean SBAS. Proc. of the Ion Pacific PNT Meeting, 2013, 8900 (6): 296- 306.
26	ZHANG Q, RUI L I, WANG Z. An improved ionospheric spatial threat model for SBAS. Chinese Journal of Electronics, 2017, 26 (5): 1105- 1110. doi: 10.1049/cje.2017.07.002
27	LIU D, FENG J, CHEN L, et al. A study on construction of ionospheric spatial threat model for China SBAS. Proc. of the China Satellite Navigation Conference, 2017: 195-208.
28	BLANCH J. Using Kriging to bound satellite ranging errors due to the ionosphere. 2004.
29	DATTA-BARUA S. Ionospheric threats to the integrity of airborne GPS users. 2008.
30	DO-229E. Minimum operational performance standards for global positioning system/satellite-based augmentation system airborne equipment. Washington, D.C.: Radio Technical Commission for Aeronautics, 2016.

Date	Kp	Dst(nT)	Geomagnetic storm class
2000.07.15	9	– 295	Extreme
2000.07.16	8	– 288	Severe
2001.03.31	9	– 378	Extreme
2001.11.24	9	– 204	Extreme
2003.10.29	9	– 339	Extreme
2003.10.30	9	– 368	Extreme
2003.10.31	8	– 240	Severe
2003.11.20	9	– 402	Extreme
2004.07.27	9	– 152	Extreme
2004.11.08	9	– 369	Extreme
2004.11.10	9	– 264	Extreme
2005.08.24	9	– 191	Extreme
2005.09.11	9	– 131	Extreme

User site	UIVEs bound		Mean reduction in UIVEs
User site	2D spatial threat model	L2D spatial threat model	Mean reduction in UIVEs
alla	100	100	46.0
brew	100	100	25.7
dwi1	100	100	47.3
gus6	100	100	37.4
ict2	100	100	47.4
ipaz	100	100	29.6
jplm	100	100	31.1
loyk	100	100	43.6
mepi	100	100	34.2
mnok	100	100	34.2
mtei	100	100	32.3
mtnt	100	100	33.0
p030	100	100	43.4
prmi	100	100	16.1
wes2	100	100	36.0

Ionospheric condition	ηVPL(5%)	ηHPL(5%)
quite (20 June 2016)	36.8	43.4
moderately disturbed(13 October 2015)	34.0	37.9
severely disturbed(17 March 2015)	35.2	28.0

Impact of ionospheric irregularity on SBAS integrity: spatial threat modeling and improvement

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