The Improvement of Integrating Low-cost Inertial Navigation System and Satellite Data Using the GMDH Neural Network

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Shokoohi Mehr Kazem; FARSHAD MOHSEN; HAVANGI RAMAZAN; MEHRSHAD NASSER

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING) Year:2020 | Volume:10 | Issue:4 Page(s): 39-52

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

719

Download:

Cites:

Information Journal Paper

Title

The Improvement of Integrating Low-cost Inertial Navigation System and Satellite Data Using the GMDH Neural Network

Author(s)

Shokoohi Mehr Kazem | FARSHAD MOHSEN | HAVANGI RAMAZAN | MEHRSHAD NASSER | Issue Writer Certificate

Keywords

data fusionQ1

inertial systemQ1

global navigation satellite systemQ1

GMDH neural networkQ2

Kalman filterQ2

Abstract

Nowadays, the approach of integrating low-cost inertial navigation system and satellite data is common due to accuracy and reliability matters. Self-reliance, high data set rates and rotational data presentation, in comparison to gradual reduced accuracy in inertial navigation systems, low data set rates, lack of rotational data presentation, and disruption or outage in the receipt of GNSS data has popularized the approach while Kalman filter integration methods face limitations such as model dependency, the need for prior knowledge, linearization, and most importantly, the inefficiency at GNSS signals outage. This paper aims to present a reliable intelligent online integration method that keeps functioning at signal interruption or outage. The results of GMDH neural network simulations and its comparison with the conventional Kalman filter method and MLP and RBF neural networks show that the former can be used in online navigation operations and for the inevitable conditions of GNSS data unavailability due to high speed and capability in estimating and correcting INS errors (because of having a simple structure and removing inactive neurons, through an effective learning method).

Cites

No record.

References

No record.

Cite

APA: Copy

Shokoohi Mehr, Kazem, FARSHAD, MOHSEN, HAVANGI, RAMAZAN, & MEHRSHAD, NASSER. (2020). The Improvement of Integrating Low-cost Inertial Navigation System and Satellite Data Using the GMDH Neural Network. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), 10(4 ), 39-52. SID. https://sid.ir/paper/203083/en

Vancouver: Copy

Shokoohi Mehr Kazem, FARSHAD MOHSEN, HAVANGI RAMAZAN, MEHRSHAD NASSER. The Improvement of Integrating Low-cost Inertial Navigation System and Satellite Data Using the GMDH Neural Network. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)[Internet]. 2020;10(4 ):39-52. Available from: https://sid.ir/paper/203083/en

IEEE: Copy

Kazem Shokoohi Mehr, MOHSEN FARSHAD, RAMAZAN HAVANGI, and NASSER MEHRSHAD, “The Improvement of Integrating Low-cost Inertial Navigation System and Satellite Data Using the GMDH Neural Network,” COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), vol. 10, no. 4 , pp. 39–52, 2020, [Online]. Available: https://sid.ir/paper/203083/en

Related Journal Papers