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

Abstract

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station (BS) and vehicles are equipped with large antenna arrays. However, radiobased 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. Building such a map is challenging, due to the inherent data association uncertainty, missed detection, and clutter. We propose a new method for cooperative vehicle positioning and mapping of the radio environment in order to address these challenges. The proposed method comprises a multimodel probability hypothesis density (PHD) filter and a map fusion routine, which is able to consider different types of objects and different fields of views (FoVs). Simulation results demonstrate that the proposed method handles the aforementioned challenges, and improves the vehicle positioning and mapping performance.

5G Positioning - A Machine Learning Approach

“…Note that for the original data set, the difference in BRSRP between the beam with strongest BRSRP and the beam with tenth strongest has an order of magnitude I: The ranking of feature importance generated by the random forest for the different data sets. The number in brackets refers to the strength of the BRSRP, e.g., BRSRP(1) refers to the BRSRP of the beam with the highest BRSRP and DoD (5) to the DoD of the beam with fifth highest BRSRP. Only the six features with highest importance are listed.…”

“…Interpolation data set Layer data set Feature Importance Feature Importance Feature Importance BRSRP(1) -BRSRP(10) 0.6196 BRSRP(4) 0.3698 BRSRP(1) -BRSRP (5) 0.3441 DoD(5) 0.0652 DoD(5) 0.1909 DoD(1) 0.1437 BRSRP(1) -BRSRP (4) 0.0348 BRSRP(1) -BRSRP(4) 0.0946 BRSRP(1) -BRSRP(4) 0.1286 DoD(4) 0.0251 BRSRP (7) 0.0331 DoD(1)-Dod (2) 0.0992 DoD(1)-DoD (2) 0.0220 BRSRP(10) 0.0318 DoD(2) 0.0703 BRSRP(1) -BRSRP (3) 0.0218 BRSRP(2) 0.0315 BRSRP(5) 0.0292 The learning data and evaluation data were selected randomly from the data sets in NLOS conditions. The performance is illustrated as the CDF of positioning error in meters.…”

“…Suggested approaches also include to model the full physical environment to estimate the average path loss and delay spread in microcellular environments [4], or to formulate the problem as a simultaneous localisation and mapping (SLAM) problem for two vehicles positioning themself in a urban area. They should also positioning each other while building up a radio map considering multipath components such as scattering and reflections of signals [5]. The radio map can later be used for positioning of UEs in the given environment.…”

5G SLAM Using the Clustering and Assignment Approach with Diffuse Multipath

“…Extension of such methods to include the hidden DAs is possible, following the approaches from Reference [22]. In Reference [23], the probability hypothesis density (PHD) filter, which is a random-finite-set filter, was used to solve the 5G SLAM problem, considering only one measurement per object. In Reference [24], a more powerful random-finite-set filter, Poisson multi-Bernoulli mixture (PMBM) filter, was used, which enumerates all possible DAs.…”

“…The novelty of the proposed approach compared to the existing random finite set (RFS) based 5G SLAM work [23,40] is three-fold-first of all, References [23,40] did not use a real channel estimator, which makes the problem easier. Secondly, they assumed at most one measurement from an object, which is not the real case.…”