5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion

Hyowon Kim, Karl Granstrom, Lin Gao, Giorgio Battistelli, Sunwoo Kim, Henk Wymeersch

Research output: Contribution to journalArticle

2 Scopus citations

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.

Original languageEnglish
Article number9032328
Pages (from-to)3782-3795
Number of pages14
JournalIEEE Transactions on Wireless Communications
Volume19
Issue number6
DOIs
StatePublished - 2020 Jun

Keywords

  • 5G millimeter-wave
  • cooperative positioning and mapping
  • map fusion
  • probability hypothesis density
  • vehicular networks

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