GNSS-only Collaborative Positioning Among Connected Vehicles

Minetto, Alex; Nardin, Andrea; Dovis, Fabio

doi:10.1145/3331054.3331552

Cited by 13 publications

(12 citation statements)

References 16 publications

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“…An estimation of d ab,k , thus of d ab,k , can be obtained through the combination of sets of GNSS raw observables shared among networked GNSS receivers. Once the time offset between two sets of measurements is compensated, DGNSS algorithms can be used to determine the inter-agent distance, as detailed in [2], [30]. The synchronization of multi-receiver measurements is out of the scope of this paper but consoli-dated strategies addressed Doppler-based linear regression to cope for the misalignment among the measurements dumping epochs of different receivers [44], [45].…”

Section: Methodology a Dgnss-based Rangingmentioning

confidence: 99%

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A Cognitive Particle Filter for Collaborative DGNSS Positioning

2020

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The advances in low-latency communications networks and the ever-growing amount of devices offering localization and navigation capabilities opened a number of opportunities to develop innovative network-based collaborative solutions to satisfy the increasing demand for positioning accuracy and precision. Recent research works indeed, have fostered the concept of networked Global Navigation Satellite System (GNSS) receivers supporting the sharing of raw measurements with other receivers within the same network. Such measurements (i.e. pseudorange and Doppler) can be processed through Differential GNSS (DGNSS) techniques to retrieve inter-agent distances which can be in turn integrated to improve positioning performance. This paper investigates an improved Bayesian estimation algorithm for a sensorless, tight-integration of DGNSS-based collaborative measurements through a modified Particle Filter (PF), namely Cognitive PF. Differently from Extended Kalman Filter and Uscented Kalman Filter indeed, a PF natively support the non-Gaussian noise distribution which characterizes DGNSS-based interagent distances. The proposed Cognitive PF is hence designed, implemented and optimized according to the architecture of a proprietary Inertial Navigation System (INS)-free Global Navigation Satellite System (GNSS) software receiver. Experimental tests performed through realistic radio-frequency GNSS signals showed a remarkable improvement in positioning accuracy w.r.t. reference PF and EKF architectures.

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Section: Methodology a Dgnss-based Rangingmentioning

confidence: 99%

“…Global Navigation Satellite Systems (GNSSs) are exploited nowadays in a wide range of applications with the aim of providing positioning and navigation capabilities to a growing number of devices [1], [2]. In parallel, Direct Short Range Communication (DSRC) and Ultra-Reliable Low-Latency Communication (URLLC) (e.g.…”

Section: Introductionmentioning

confidence: 99%

A Cognitive Particle Filter for Collaborative DGNSS Positioning

2020

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“…Other DGNSSbased techniques are comparatively investigated in [25]. A further method was then proposed by relying on the exchange of both pseudorange measurements and the terms of the Direction Cosine Matrix (DCM) computed within the Position Time Velocity (PVT), in [26].In previous contributions, researchers observed remarkable improvement in CP by tightly integrating Double Differences (DD), and the quantity of information carried by such differential measurements was demonstrated in [22], [27]- [29].…”

Section: A Estimation and Integration Of Gnss Differential Range Measurementsmentioning

confidence: 99%

“…By coupling the disclosure of raw GNSS data in smartphones and tablets and the recent advances in communication networks (e.g. Ultra-Reliable and Low-Latency Communications (URLLC) in 5G NR), the concept of networked GNSS receiver is expected to rise to mass-market and IoT applications [21]. This trend enables the exchange and integration of satellite-based relative measurements among connected devices, thus the implementation of CP solutions.…”

Section: Introductionmentioning

confidence: 99%

“…The implementation proposed in this paper foresees the mutual exchange of GNSS raw measurements obtained from integrated ultra-low-cost receivers for a near-real-time computation of the inter-agent distances. The cooperation improves the quality of their positioning through a hybrid navigation scheme proposed in [22], that integrates such auxiliary interagent distances in a peer-to-peer fashion. A demonstrator was developed in the context of the European Space Agency (ESA) HANSEL project to assess the feasibility and the effectiveness of GNSS-based CP using consumer-grade smartphones [23], by relying on their access to raw GNSS measurements.…”

Section: Introductionmentioning

confidence: 99%

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DGNSS Cooperative Positioning in Mobile Smart Devices: a Proof of Concept

Minetto¹,

Bello²,

Dovis³

2021

Preprint

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<div>In recent years positioning and navigation capabilities in mobile devices have become essential to the evergrowing number of position-related smart applications. Global Navigation Satellite System (GNSS) constitutes the provider for geo-localization, therefore consumer-grade, embedded GNSS receivers have become ubiquitous in mobile smart devices. Among these, smartphones play a dominant role in enabling such modern services based on position information. However, GNSS positioning shows several weaknesses in urban contexts where mobile smart devices are massively diffused. Indeed, the limited sky visibility and multipath scattering induced by buildings severely threat the quality of the final solution. Two main ingredients can enable innovative collaborative strategies capable to increase the robustness of GNSS navigation: The availability of raw GNSS measurements which have been recently disclosed in ultra-low cost smartphone chipsets and the ubiquitous connectivity provided by modern low-latency, network infrastructures allowing for near-real-time exchange of data. This work presents the architecture of a Proof Of Concept designed to demonstrate the feasibility of a GNSS-only Cooperative Positioning among networked smartphones equipped with GNSS receivers. The test campaign presented in this work assessed the feasibility of the approach over 4G/LTE network connectivity and an average accuracy improvement over the 40%.</div>

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