Error Prediction Model of Klobuchar Ionospheric Delay Based on TS Fuzzy Neural Network

Yaqi Peng; Chengdong Xu; Fei Niu; Yiwen Wang

doi:10.1007/978-981-13-7759-4_24

Error Prediction Model of Klobuchar Ionospheric Delay Based on TS Fuzzy Neural Network

Yaqi Peng^*, Chengdong Xu, Fei Niu, Yiwen Wang

^*Corresponding author for this work

School of Aerospace Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

The ionospheric delay has a very important influence on the positioning accuracy of satellite navigation. It can be effectively reduced by establishing an accurate and reasonable ionospheric correction model. At present, Klobuchar parameter model is widely used in single-frequency receiver, but the correction rate of this model can only reach about 60%, which can not meet the need of high precision navigation and positioning. Through the research and analysis of the ionospheric error data of the Klobuchar parameter model, it is found that there are some periodic phenomena objectively. Aiming at the error information which cannot be represented by definite mathematical model, a TS (Takagi-Sugeno) fuzzy neural network prediction model applied to Klobuchar ionospheric delay error is established by combining TS fuzzy theory with neural network. The simulation results show that the model has good fitting ability and prediction effect on the Klobuchar ionospheric delay error. Using this model to provide error compensation for the ionospheric delay can reduce the error by about 20%. It is of great significance to improve the accuracy of navigation and positioning.

Original language	English
Title of host publication	China Satellite Navigation Conference CSNC 2019 Proceedings - Volume 2
Editors	Yuanxi Yang, Changfeng Yang, Jiadong Sun
Publisher	Springer Verlag
Pages	266-274
Number of pages	9
ISBN (Print)	9789811377587
DOIs	https://doi.org/10.1007/978-981-13-7759-4_24
Publication status	Published - 2019
Event	10th China Satellite Navigation Conference, CSNC 2019 - Beijing, China Duration: 22 May 2019 → 25 May 2019

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	563
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	10th China Satellite Navigation Conference, CSNC 2019
Country/Territory	China
City	Beijing
Period	22/05/19 → 25/05/19

Keywords

Error prediction
Fuzzy neural network
Ionospheric delay
Klobuchar model

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10.1007/978-981-13-7759-4_24

Cite this

Peng, Y., Xu, C., Niu, F., & Wang, Y. (2019). Error Prediction Model of Klobuchar Ionospheric Delay Based on TS Fuzzy Neural Network. In Y. Yang, C. Yang, & J. Sun (Eds.), China Satellite Navigation Conference CSNC 2019 Proceedings - Volume 2 (pp. 266-274). (Lecture Notes in Electrical Engineering; Vol. 563). Springer Verlag. https://doi.org/10.1007/978-981-13-7759-4_24

@inproceedings{6c761f478a4240d59038cdf22509f1c2,

title = "Error Prediction Model of Klobuchar Ionospheric Delay Based on TS Fuzzy Neural Network",

abstract = "The ionospheric delay has a very important influence on the positioning accuracy of satellite navigation. It can be effectively reduced by establishing an accurate and reasonable ionospheric correction model. At present, Klobuchar parameter model is widely used in single-frequency receiver, but the correction rate of this model can only reach about 60%, which can not meet the need of high precision navigation and positioning. Through the research and analysis of the ionospheric error data of the Klobuchar parameter model, it is found that there are some periodic phenomena objectively. Aiming at the error information which cannot be represented by definite mathematical model, a TS (Takagi-Sugeno) fuzzy neural network prediction model applied to Klobuchar ionospheric delay error is established by combining TS fuzzy theory with neural network. The simulation results show that the model has good fitting ability and prediction effect on the Klobuchar ionospheric delay error. Using this model to provide error compensation for the ionospheric delay can reduce the error by about 20%. It is of great significance to improve the accuracy of navigation and positioning.",

keywords = "Error prediction, Fuzzy neural network, Ionospheric delay, Klobuchar model",

author = "Yaqi Peng and Chengdong Xu and Fei Niu and Yiwen Wang",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; 10th China Satellite Navigation Conference, CSNC 2019 ; Conference date: 22-05-2019 Through 25-05-2019",

year = "2019",

doi = "10.1007/978-981-13-7759-4_24",

language = "English",

isbn = "9789811377587",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

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booktitle = "China Satellite Navigation Conference CSNC 2019 Proceedings - Volume 2",

address = "Germany",

}

Peng, Y, Xu, C, Niu, F & Wang, Y 2019, Error Prediction Model of Klobuchar Ionospheric Delay Based on TS Fuzzy Neural Network. in Y Yang, C Yang & J Sun (eds), China Satellite Navigation Conference CSNC 2019 Proceedings - Volume 2. Lecture Notes in Electrical Engineering, vol. 563, Springer Verlag, pp. 266-274, 10th China Satellite Navigation Conference, CSNC 2019, Beijing, China, 22/05/19. https://doi.org/10.1007/978-981-13-7759-4_24

Error Prediction Model of Klobuchar Ionospheric Delay Based on TS Fuzzy Neural Network. / Peng, Yaqi; Xu, Chengdong; Niu, Fei et al.
China Satellite Navigation Conference CSNC 2019 Proceedings - Volume 2. ed. / Yuanxi Yang; Changfeng Yang; Jiadong Sun. Springer Verlag, 2019. p. 266-274 (Lecture Notes in Electrical Engineering; Vol. 563).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Error Prediction Model of Klobuchar Ionospheric Delay Based on TS Fuzzy Neural Network

AU - Peng, Yaqi

AU - Xu, Chengdong

AU - Niu, Fei

AU - Wang, Yiwen

PY - 2019

Y1 - 2019

N2 - The ionospheric delay has a very important influence on the positioning accuracy of satellite navigation. It can be effectively reduced by establishing an accurate and reasonable ionospheric correction model. At present, Klobuchar parameter model is widely used in single-frequency receiver, but the correction rate of this model can only reach about 60%, which can not meet the need of high precision navigation and positioning. Through the research and analysis of the ionospheric error data of the Klobuchar parameter model, it is found that there are some periodic phenomena objectively. Aiming at the error information which cannot be represented by definite mathematical model, a TS (Takagi-Sugeno) fuzzy neural network prediction model applied to Klobuchar ionospheric delay error is established by combining TS fuzzy theory with neural network. The simulation results show that the model has good fitting ability and prediction effect on the Klobuchar ionospheric delay error. Using this model to provide error compensation for the ionospheric delay can reduce the error by about 20%. It is of great significance to improve the accuracy of navigation and positioning.

AB - The ionospheric delay has a very important influence on the positioning accuracy of satellite navigation. It can be effectively reduced by establishing an accurate and reasonable ionospheric correction model. At present, Klobuchar parameter model is widely used in single-frequency receiver, but the correction rate of this model can only reach about 60%, which can not meet the need of high precision navigation and positioning. Through the research and analysis of the ionospheric error data of the Klobuchar parameter model, it is found that there are some periodic phenomena objectively. Aiming at the error information which cannot be represented by definite mathematical model, a TS (Takagi-Sugeno) fuzzy neural network prediction model applied to Klobuchar ionospheric delay error is established by combining TS fuzzy theory with neural network. The simulation results show that the model has good fitting ability and prediction effect on the Klobuchar ionospheric delay error. Using this model to provide error compensation for the ionospheric delay can reduce the error by about 20%. It is of great significance to improve the accuracy of navigation and positioning.

KW - Error prediction

KW - Fuzzy neural network

KW - Ionospheric delay

KW - Klobuchar model

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U2 - 10.1007/978-981-13-7759-4_24

DO - 10.1007/978-981-13-7759-4_24

M3 - Conference contribution

AN - SCOPUS:85065900790

SN - 9789811377587

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SP - 266

EP - 274

BT - China Satellite Navigation Conference CSNC 2019 Proceedings - Volume 2

A2 - Yang, Yuanxi

A2 - Yang, Changfeng

A2 - Sun, Jiadong

PB - Springer Verlag

T2 - 10th China Satellite Navigation Conference, CSNC 2019

Y2 - 22 May 2019 through 25 May 2019

ER -

Error Prediction Model of Klobuchar Ionospheric Delay Based on TS Fuzzy Neural Network

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