Bibliographic Details
| Title: |
A Novel Hybrid of a Fading Filter and an Extreme Learning Machine for GPS/INS during GPS Outages |
| Authors: |
Di Wang, Xiaosu Xu, Yongyun Zhu |
| Source: |
Sensors, Vol 18, Iss 11, p 3863 (2018) |
| Publisher Information: |
MDPI AG, 2018. |
| Publication Year: |
2018 |
| Collection: |
LCC:Chemical technology |
| Subject Terms: |
fading filter, extreme learning machine, GPS/INS, integrated navigation, Chemical technology, TP1-1185 |
| More Details: |
In this paper, a novel algorithm based on the combination of a fading filter (FF) and an extreme learning machine (ELM) is presented for Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. In order to increase the filtering accuracy of the model, a variable fading factor fading filter based on the fading factor is proposed. It adjusts the fading factor by the ratio of the estimated covariance before and after the moment which proves to have excellent performance in our experiment. An extreme learning machine based on a Fourier orthogonal basis function is introduced that considers the deterioration of the accuracy of the navigation system during GPS outages and has a higher positioning accuracy and faster learning speed than the typical neural network learning algorithm. In the end, a simulation and real road test are performed to verify the effectiveness of this algorithm. The results show that the accuracy of the fading filter based on a variable fading factor is clearly improved, and the proposed improved ELM algorithm can provide position corrections during GPS outages more effectively than the other algorithms (ELM and the traditional radial basis function neural network). |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1424-8220 |
| Relation: |
https://www.mdpi.com/1424-8220/18/11/3863; https://doaj.org/toc/1424-8220 |
| DOI: |
10.3390/s18113863 |
| Access URL: |
https://doaj.org/article/ecc9a83a5eec4d0688bc3a5d977179ef |
| Accession Number: |
edsdoj.9a83a5eec4d0688bc3a5d977179ef |
| Database: |
Directory of Open Access Journals |
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