Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine
A high-resolution digital elevation model (DEM) is an important element that determines the performance of terrain referenced navigation (TRN). However, the higher the resolution of the DEM, the bigger the memory size needed for storing it. It is difficult to secure such large memory spaces in small...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/9142694 |
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| _version_ | 1850235303547109376 |
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| author | Jungshin Lee Changky Sung Juhyun Oh |
| author_facet | Jungshin Lee Changky Sung Juhyun Oh |
| author_sort | Jungshin Lee |
| collection | DOAJ |
| description | A high-resolution digital elevation model (DEM) is an important element that determines the performance of terrain referenced navigation (TRN). However, the higher the resolution of the DEM, the bigger the memory size needed for storing it. It is difficult to secure such large memory spaces in small, low-priced unmanned aerial vehicles. In this study, a high-precision terrain regression model to fit the DEM is generated using the extreme learning machine technique based on the multilayer radial basis function. The TRN results using the proposed method are compared with existing studies on various DEM fitting methods. This study verifies that the proposed method obtains improved fitting accuracy and TRN performance over existing DEM fitting methods such as bilinear interpolation, SVM for regression, and bi-spline neural network, without the DEM storage space. |
| format | Article |
| id | doaj-art-103c5bee41e545df9d2116773c4b954c |
| institution | OA Journals |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-103c5bee41e545df9d2116773c4b954c2025-08-20T02:02:19ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742019-01-01201910.1155/2019/91426949142694Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning MachineJungshin Lee0Changky Sung1Juhyun Oh2The 3rd R&D Institute 4th Center, Agency for Defense Development, Daejeon 34060, Republic of KoreaThe 3rd R&D Institute 4th Center, Agency for Defense Development, Daejeon 34060, Republic of KoreaThe 3rd R&D Institute 4th Center, Agency for Defense Development, Daejeon 34060, Republic of KoreaA high-resolution digital elevation model (DEM) is an important element that determines the performance of terrain referenced navigation (TRN). However, the higher the resolution of the DEM, the bigger the memory size needed for storing it. It is difficult to secure such large memory spaces in small, low-priced unmanned aerial vehicles. In this study, a high-precision terrain regression model to fit the DEM is generated using the extreme learning machine technique based on the multilayer radial basis function. The TRN results using the proposed method are compared with existing studies on various DEM fitting methods. This study verifies that the proposed method obtains improved fitting accuracy and TRN performance over existing DEM fitting methods such as bilinear interpolation, SVM for regression, and bi-spline neural network, without the DEM storage space.http://dx.doi.org/10.1155/2019/9142694 |
| spellingShingle | Jungshin Lee Changky Sung Juhyun Oh Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine International Journal of Aerospace Engineering |
| title | Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine |
| title_full | Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine |
| title_fullStr | Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine |
| title_full_unstemmed | Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine |
| title_short | Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine |
| title_sort | terrain referenced navigation using a multilayer radial basis function based extreme learning machine |
| url | http://dx.doi.org/10.1155/2019/9142694 |
| work_keys_str_mv | AT jungshinlee terrainreferencednavigationusingamultilayerradialbasisfunctionbasedextremelearningmachine AT changkysung terrainreferencednavigationusingamultilayerradialbasisfunctionbasedextremelearningmachine AT juhyunoh terrainreferencednavigationusingamultilayerradialbasisfunctionbasedextremelearningmachine |