Joint Vehicle Tracking and RSU Selection for V2I Communications With Extended Kalman Filteropen access
- Authors
- Song, Jiho; Hyun, Seong-Hwan; Lee, Jong-Ho; Choi, Jeongsik; Kim, Seong-Cheol
- Issue Date
- May-2022
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Radar tracking; Vehicle-to-infrastructure; Measurement; Channel estimation; Tracking; Signal to noise ratio; Kalman filters; Joint vehicle tracking; road side unit selection; extended Kalman filter; millimeter wave V2I communications
- Citation
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.71, no.5, pp.5609 - 5614
- Journal Title
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Volume
- 71
- Number
- 5
- Start Page
- 5609
- End Page
- 5614
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/43611
- DOI
- 10.1109/TVT.2022.3153345
- ISSN
- 0018-9545
- Abstract
- We develop joint vehicle tracking and road side unit (RSU) selection algorithms suitable for vehicle-to-infrastructure (V2I) communications. We first design an analytical framework for evaluating vehicle tracking systems based on the extended Kalman filter. A simple, yet effective, metric that quantifies the vehicle tracking performance is derived in terms of the angular derivative of a dominant spatial frequency. Second, an RSU selection algorithm is proposed to select a proper RSU that enhances the vehicle tracking performance. A joint vehicle tracking algorithm is also developed to maximize the tracking performance by considering sounding samples at multiple RSUs while minimizing the amount of sample exchange. The numerical results verify that the proposed vehicle tracking algorithms give better performance than conventional signal-to-noise ratio-based tracking systems.
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