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- Kim, Hyowon, et al.
(författare)
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5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion
- 2020
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Ingår i: IEEE Transactions on Wireless Communications. - 1558-2248 .- 1536-1276. ; 19:6, s. 3782-3795
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Tidskriftsartikel (refereegranskat)abstract
- 5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. We propose a new method for cooperative vehicle positioning and mapping of the radio environment, comprising a multiple-model probability hypothesis density filter and a map fusion routine, which is able to consider different types of objects and different fields of views. Simulation results demonstrate the performance of the proposed method.
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| 2. |
- Kim, Hyowon, et al.
(författare)
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Joint CKF-PHD Filter and Map Fusion for 5G Multi-cell SLAM
- 2020
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Ingår i: IEEE International Conference on Communications. - 1550-3607. ; 2020-June
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Konferensbidrag (refereegranskat)abstract
- 5G is expected to enable simultaneous vehicle localization and environment mapping (SLAM). Furthermore, vehicular networks will be covered with 5G small cells, wherein the map information is collected at each base station (BS) and then fused so as to promote the overall performance of SLAM. In 5G multi-cell SLAM, there are challenges such as the unknown number of targets, uncertainty regarding the association between the targets and the measurements, unknown types of targets, as well as map management among BSs. To address those challenges, we propose a new method for 5G multi-cell SLAM which comprises a joint cubature Kalman filter and multi-model probability hypothesis density, and a map fusion routine. Simulation results demonstrate that the proposed method solves the aforementioned challenges and also improves vehicle state and map estimates.
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