Multi-scale Vehicle Localization In Heterogeneous Mobile Communication Networks

TL;DR

This paper introduces a multi-scale vehicle localization method in heterogeneous mobile networks that reduces computational complexity and enhances accuracy by segmenting roads and extracting salient features, enabling real-time, high-precision vehicle positioning.

Contribution

It proposes a novel road-aware localization mechanism with SP segmentation and SF extraction, significantly improving real-time vehicle positioning accuracy and efficiency in complex environments.

Findings

Achieves lower latency compared to benchmark schemes

Ensures high positioning accuracy verified by CRLB analysis

Effective in complex road environments with reduced computational load

Abstract

Low-latency and high-precision vehicle localization plays a significant role in enhancing traffic safety and improving traffic management for intelligent transportation. However, in complex road environments, the low latency and high precision requirements could not always be fulfilled due to the high complexity of localization computation. To tackle this issue, we propose a road-aware localization mechanism in heterogeneous networks (HetNet) of the mobile communication system, which enables real-time acquisition of vehicular position information, including the vehicular current road, segment within the road, and coordinates. By employing this multi-scale localization approach, the computational complexity can be greatly reduced while ensuring accurate positioning. Specifically, to reduce positioning search complexity and ensure positioning precision, roads are partitioned into low-dimensional segments with unequal lengths by the proposed singular point (SP) segmentation method. To reduce feature-matching complexity, distinctive salient features (SFs) are extracted sparsely representing roads and segments, which can eliminate redundant features while maximizing the feature information gain. The Cram\'er-Rao Lower Bound (CRLB) of vehicle positioning errors is derived to verify the positioning accuracy improvement brought from the segment partition and SF extraction. Additionally, through SF matching by integrating the inclusion and adjacency position relationships, a multi-scale vehicle localization (MSVL) algorithm is proposed to identify vehicular road signal patterns and determine the real-time segment and coordinates. Simulation results show that the proposed multi-scale localization mechanism can achieve lower latency and high precision compared to the benchmark schemes.