Multipath modeling and mitigation by using sparse estimation in global navigation satellite system-challenged urban vehicular environments

Yue Yuan,Feng Shen,Dingjie Xu

doi:10.1177/1729881420968696

Yue Yuan, Feng Shen + Show 1 more

Open Access

https://doi.org/10.1177/1729881420968696

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Abstract

Multipath interference has been one of the most difficult problems when using global navigation satellite system-based vehicular navigation in urban environments. In this article, we develop a multipath mitigation algorithm exploiting the sparse estimation theory that improves the absolute positioning accuracy in urban environments. The navigation observation model is established by considering the multipath bias as additive positioning errors, and the assumption for the proposed method is that global navigation satellite system signals contaminated due to multipath are the minority among the received signals, which makes the unknown bias vector sparse. We investigated an improved elastic net method to estimate the sparse multipath bias vector, and the global navigation satellite system measurements can be corrected by subtracting the estimated multipath error. The positioning performance of the proposed method is verified by analytical and experimental results.

Highlights

In recent years, with the rapid development of the economy, automobiles have gradually become the main means of transportation in daily life
Vehicular navigation technologies have become a vital component in the intelligent transportation system for they provide convenience, location-related information, and safety to transportation users.[1,2]
The urban environment presents great challenges to common commercial Global navigation satellite system (GNSS) receivers while vehicular positioning.[6,7,8,9]. This is mainly because the GNSS positioning performance can be severely degraded by the multipath

Summary

Introduction

With the rapid development of the economy, automobiles have gradually become the main means of transportation in daily life. The improved elastic net problem is described, and the solution that can be used to estimate multipath bias vector is investigated.

Results

Conclusion