GNSS Differential Code Bias Determination Using Rao‐Blackwellized Particle Filtering
Abstract The Assimilative Canadian High Arctic Ionospheric Model (A‐CHAIM) is a near‐real‐time data assimilation model of the high latitude ionosphere, incorporating measurements from many instruments, including slant Total Electron Content measurements from ground‐based Global Navigation Satellite...
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| Format: | Article |
| Language: | English |
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Wiley
2024-05-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003611 |
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| author | Ben Reid David R. Themens Anthony McCaffrey P. T. Jayachandran Mainul Hoque Andrew J. Mazzella Jr. |
| author_facet | Ben Reid David R. Themens Anthony McCaffrey P. T. Jayachandran Mainul Hoque Andrew J. Mazzella Jr. |
| author_sort | Ben Reid |
| collection | DOAJ |
| description | Abstract The Assimilative Canadian High Arctic Ionospheric Model (A‐CHAIM) is a near‐real‐time data assimilation model of the high latitude ionosphere, incorporating measurements from many instruments, including slant Total Electron Content measurements from ground‐based Global Navigation Satellite System (GNSS) receivers. These measurements have receiver‐specific Differential Code Biases (DCB) which must be resolved to produce an absolute measurement, which are resolved simultaneously with the ionospheric state using Rao‐Blackwellized particle filtering. These DCBs are compared to published values and to DCBs determined using eight different Global Ionospheric Maps (GIM), which show small but consistent systematic differences. The potential cause of these systematic biases is investigated using multiple experimental A‐CHAIM test runs, including the effect of plasmaspheric electron content. By running tests using the GIM‐derived DCBs, it is shown that using A‐CHAIM DCBs produces the lowest overall error, and that using GIM DCBs causes an overestimation of the topside electron density which can exceed 100% when compared to in situ measurements from DMSP. |
| format | Article |
| id | doaj-art-222be6f2bd3a4685ac4a68b040b452bb |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-222be6f2bd3a4685ac4a68b040b452bb2025-01-14T16:27:30ZengWileySpace Weather1542-73902024-05-01225n/an/a10.1029/2023SW003611GNSS Differential Code Bias Determination Using Rao‐Blackwellized Particle FilteringBen Reid0David R. Themens1Anthony McCaffrey2P. T. Jayachandran3Mainul Hoque4Andrew J. Mazzella Jr.5Department of Physics University of New Brunswick Fredericton NB CanadaSchool of Engineering SERENE University of Birmingham Birmingham UKDepartment of Physics University of New Brunswick Fredericton NB CanadaDepartment of Physics University of New Brunswick Fredericton NB CanadaGerman Aerospace Center (DLR) Neustrelitz GermanyNone Watertown Massachusetts USAAbstract The Assimilative Canadian High Arctic Ionospheric Model (A‐CHAIM) is a near‐real‐time data assimilation model of the high latitude ionosphere, incorporating measurements from many instruments, including slant Total Electron Content measurements from ground‐based Global Navigation Satellite System (GNSS) receivers. These measurements have receiver‐specific Differential Code Biases (DCB) which must be resolved to produce an absolute measurement, which are resolved simultaneously with the ionospheric state using Rao‐Blackwellized particle filtering. These DCBs are compared to published values and to DCBs determined using eight different Global Ionospheric Maps (GIM), which show small but consistent systematic differences. The potential cause of these systematic biases is investigated using multiple experimental A‐CHAIM test runs, including the effect of plasmaspheric electron content. By running tests using the GIM‐derived DCBs, it is shown that using A‐CHAIM DCBs produces the lowest overall error, and that using GIM DCBs causes an overestimation of the topside electron density which can exceed 100% when compared to in situ measurements from DMSP.https://doi.org/10.1029/2023SW003611data assimilationGNSSdifferential code biasparticle filterRao‐Blackwellizedreal time |
| spellingShingle | Ben Reid David R. Themens Anthony McCaffrey P. T. Jayachandran Mainul Hoque Andrew J. Mazzella Jr. GNSS Differential Code Bias Determination Using Rao‐Blackwellized Particle Filtering Space Weather data assimilation GNSS differential code bias particle filter Rao‐Blackwellized real time |
| title | GNSS Differential Code Bias Determination Using Rao‐Blackwellized Particle Filtering |
| title_full | GNSS Differential Code Bias Determination Using Rao‐Blackwellized Particle Filtering |
| title_fullStr | GNSS Differential Code Bias Determination Using Rao‐Blackwellized Particle Filtering |
| title_full_unstemmed | GNSS Differential Code Bias Determination Using Rao‐Blackwellized Particle Filtering |
| title_short | GNSS Differential Code Bias Determination Using Rao‐Blackwellized Particle Filtering |
| title_sort | gnss differential code bias determination using rao blackwellized particle filtering |
| topic | data assimilation GNSS differential code bias particle filter Rao‐Blackwellized real time |
| url | https://doi.org/10.1029/2023SW003611 |
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