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

Abstract

Global Navigation Satellite System (GNSS) constitutes the foremost provider for geo-localization in a growing number of consumer-grade applications and services supporting urban mobility. Therefore, low-cost and ultra-low-cost, embedded GNSS receivers have become ubiquitous in mobile devices such as smartphones and consumer electronics to a large extent. However, limited sky visibility and multipath scattering induced in urban areas hinder positioning and navigation capabilities, thus threatening the quality of position estimates. This work leverages the availability of raw GNSS measurements in ultra-low-cost smartphone chipsets and the ubiquitous connectivity provided by modern, low-latency network infrastructures to enable a Cooperative Positioning (CP) framework. A Proof Of Concept is presented that aims at demonstrating the feasibility of a GNSSonly CP among networked smartphones embedding ultra-lowcost GNSS receivers. The test campaign presented in this study assessed the feasibility of a client-server approach over 4G/LTE network connectivity. Results demonstrated an overall service availability above 80%, and an average accuracy improvement over the 40% w.r.t. to the GNSS standalone solution.

Factor Graph Weight Particles aided Distributed Underwater Cooperative Positioning Algorithm

“…Here, the EKF is used for CP tight integration. The details about the GNSS-based CP method can be found in [10].  Generalized approximate message passing (GAMP): This method can make full use of GNSS pseudoranges, Doppler shifts, inter-node ranging measurements and > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < measurements of other sensors inside the CP system by applying GAMP.…”

“…Based on different techniques for inter-node ranging, the CP methods can be divided into two categories, including GNSS-based CP methods and range-based CP methods [9]. The GNSS-based CP methods mainly apply the double difference (DD) on the pseudorange [10,11] or carrier phase measurements [12,13] to obtain inter-node distances or relative positions between two vehicles. Although GNSS DD techniques have been used by many researchers for inter-node ranging in CP methods, the availability and accuracy of double differenced GNSS is limited in dense urban environments due to multipath effects, NLOS delays and signal outages [14,15].…”

Cooperative D-GNSS Aided with Multi Attribute Decision Making Module: A Rigorous Comparative Analysis

“…The expansion of GPS, the addition of new satellite navigation systems, and the attainment of higher sampling rates (>10 Hz) have greatly benefited low-cost GNSS receivers. They typically support a single-or dual-band functionality, have small size and mass, can operate with lowdrain batteries, can form wireless networks, and are not dependent on temperature conditions while a patch-antenna is attached to them [10,18,19].…”

“…Interruptions of satellite positioning regarding signal receptions in obstructed skies and complex propagation environments will require coupling with communication technologies and other sensors for more demanding applications [10,11]. Fusion with other onboard sensors, such as inertial sensors, lidars, ultra-wideband, and cameras, will be inevitable for both categories in order to fulfill the requirements of the applications [10,11]. However, Inertial Navigation System (INS)/GNSS integration can come at a prohibitive cost for mass deployment, has limited sensory coverage, and is unsuitable for small handheld devices.…”

Improving GNSS Spoofing Awareness in Smartphones via Statistical Processing of Raw Measurements

“…These benefits have been demonstrated for code-based, aided, differential and precise point positioning [5]- [7]. Furthermore, collaborative navigation approaches leveraged raw measurements to offer a naive collaborative distancing technique [8] and a GNSS-only enhancement of Position, Velocity, Timing (PVT) estimation accuracy [9]. Although the standalone position computed from raw GNSS data may not be as accurate as the ones obtained through an onboard sensor and network integration (i.e., Google Fused Location Provider), the use of raw GNSS data and adhoc implemented algorithms can improve the solution with respect to unaided, standalone GNSS solutions.…”