A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM
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| Názov: | A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM |
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| Autori: | Ge, Yu, 1995, Kaltiokallio, Ossi, Kim, Hyowon, 1987, Jiang, Fan, 1987, Talvitie, Jukka, Valkama, M., Svensson, Lennart, 1976, Kim, Sunwoo, Wymeersch, Henk, 1976 |
| Zdroj: | 5G mobil positionering för fordonssäkerhet IEEE Journal on Selected Areas in Communications. 40(7):2179-2192 |
| Predmety: | Complexity theory, mmWave sensing, simultaneous localization and mapping, Poisson multi-Bernoulli mixture filter, Bistatic sensing, Simultaneous localization and mapping, Kalman filters, Filtering algorithms, Sensors, Computational modeling, extended Kalman filter, Receivers |
| Popis: | Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the propagation channel. To solve the SLAM problem, existing approaches rely on sigma-point or particle-based approximations, leading to high computational complexity, precluding real-time execution. We propose a novel low-complexity SLAM filter, based on the Poisson multi-Bernoulli mixture (PMBM) filter. It utilizes the extended Kalman (EK) first-order Taylor series based Gaussian approximation of the filtering distribution, and applies the track-oriented marginal multi-Bernoulli/Poisson (TOMB/P) algorithm to approximate the resulting PMBM as a Poisson multi-Bernoulli (PMB). The filter can account for different landmark types in radio SLAM and multiple data association hypotheses. Hence, it has an adjustable complexity/performance trade-off. Simulation results show that the developed SLAM filter can greatly reduce the computational cost, while it keeps the good performance of mapping and user state estimation. |
| Popis súboru: | electronic |
| Prístupová URL adresa: | https://research.chalmers.se/publication/529031 https://research.chalmers.se/publication/529031/file/529031_Fulltext.pdf |
| Databáza: | SwePub |
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Nájsť tento článok vo Web of Science | Abstrakt: | Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the propagation channel. To solve the SLAM problem, existing approaches rely on sigma-point or particle-based approximations, leading to high computational complexity, precluding real-time execution. We propose a novel low-complexity SLAM filter, based on the Poisson multi-Bernoulli mixture (PMBM) filter. It utilizes the extended Kalman (EK) first-order Taylor series based Gaussian approximation of the filtering distribution, and applies the track-oriented marginal multi-Bernoulli/Poisson (TOMB/P) algorithm to approximate the resulting PMBM as a Poisson multi-Bernoulli (PMB). The filter can account for different landmark types in radio SLAM and multiple data association hypotheses. Hence, it has an adjustable complexity/performance trade-off. Simulation results show that the developed SLAM filter can greatly reduce the computational cost, while it keeps the good performance of mapping and user state estimation. |
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| ISSN: | 07338716 15580008 |
| DOI: | 10.1109/JSAC.2022.3155504 |