Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning
In multipath assisted positioning, multipath components arriving at a receiver are regarded as being transmitted by a virtual transmitter in a line-of-sight condition. As the locations and clock offsets of the virtual and physical transmitters are in general unknown, simultaneous localization and ma...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2018/4761601 |
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| author | Markus Ulmschneider Christian Gentner Thomas Jost Armin Dammann |
| author_facet | Markus Ulmschneider Christian Gentner Thomas Jost Armin Dammann |
| author_sort | Markus Ulmschneider |
| collection | DOAJ |
| description | In multipath assisted positioning, multipath components arriving at a receiver are regarded as being transmitted by a virtual transmitter in a line-of-sight condition. As the locations and clock offsets of the virtual and physical transmitters are in general unknown, simultaneous localization and mapping (SLAM) schemes can be applied to simultaneously localize a user and estimate the states of physical and virtual transmitters as landmarks. Hence, multipath assisted positioning enables localizing a user with only one physical transmitter depending on the scenario. In this paper, we present and derive a novel filtering approach for our multipath assisted positioning algorithm called Channel-SLAM. Making use of Rao-Blackwellization, the location of a user is tracked by a particle filter, and each landmark is represented by a sum of Gaussian probability density functions, whose parameters are estimated by unscented Kalman filters. Since data association, that is, finding correspondences among landmarks, is essential for robust long-term SLAM, we also derive a data association scheme. We evaluate our filtering approach for multipath assisted positioning by simulations in an urban scenario and by outdoor measurements. |
| format | Article |
| id | doaj-art-6e6cad8e7aae41e9b3e92f2bcf21412b |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-6e6cad8e7aae41e9b3e92f2bcf21412b2025-02-03T05:51:48ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552018-01-01201810.1155/2018/47616014761601Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted PositioningMarkus Ulmschneider0Christian Gentner1Thomas Jost2Armin Dammann3German Aerospace Center (DLR), Institute of Communications and Navigation, Muenchner Str. 20, 82334 Wessling, GermanyGerman Aerospace Center (DLR), Institute of Communications and Navigation, Muenchner Str. 20, 82334 Wessling, GermanyGerman Aerospace Center (DLR), Institute of Communications and Navigation, Muenchner Str. 20, 82334 Wessling, GermanyGerman Aerospace Center (DLR), Institute of Communications and Navigation, Muenchner Str. 20, 82334 Wessling, GermanyIn multipath assisted positioning, multipath components arriving at a receiver are regarded as being transmitted by a virtual transmitter in a line-of-sight condition. As the locations and clock offsets of the virtual and physical transmitters are in general unknown, simultaneous localization and mapping (SLAM) schemes can be applied to simultaneously localize a user and estimate the states of physical and virtual transmitters as landmarks. Hence, multipath assisted positioning enables localizing a user with only one physical transmitter depending on the scenario. In this paper, we present and derive a novel filtering approach for our multipath assisted positioning algorithm called Channel-SLAM. Making use of Rao-Blackwellization, the location of a user is tracked by a particle filter, and each landmark is represented by a sum of Gaussian probability density functions, whose parameters are estimated by unscented Kalman filters. Since data association, that is, finding correspondences among landmarks, is essential for robust long-term SLAM, we also derive a data association scheme. We evaluate our filtering approach for multipath assisted positioning by simulations in an urban scenario and by outdoor measurements.http://dx.doi.org/10.1155/2018/4761601 |
| spellingShingle | Markus Ulmschneider Christian Gentner Thomas Jost Armin Dammann Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning Journal of Electrical and Computer Engineering |
| title | Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning |
| title_full | Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning |
| title_fullStr | Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning |
| title_full_unstemmed | Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning |
| title_short | Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning |
| title_sort | rao blackwellized gaussian sum particle filtering for multipath assisted positioning |
| url | http://dx.doi.org/10.1155/2018/4761601 |
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