A Cognitive Particle Filter for Collaborative DGNSS Positioning

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 article 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 inter-agent 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

Modelling and Experimental Assessment of Inter-Personal Distancing Based on Shared GNSS Observables

In the last few years, all countries worldwide have fought the spread of SARS-CoV-2 (COVID-19) by exploiting Information and Communication Technologies (ICT) to perform contact tracing. In parallel, the pandemic has highlighted the relevance of mobility and social distancing among citizens. The monitoring of such aspects appeared prominent for reactive decision-making and the effective tracking of the infection chain. In parallel to the proximity sensing among people, indeed, the concept of social distancing has captured the attention to signal processing algorithms enabling short-to-medium range distance estimation to provide behavioral models in the emergency. By exploiting the availability of smart devices, the synergy between mobile network connectivity and Global Navigation Satellite Systems (GNSS), cooperative ranging approaches allow computing inter-personal distance measurements in outdoor environments through the exchange of light-weight navigation data among interconnected users. In this paper, a model for Inter-Agent Ranging (IAR) is provided and experimentally assessed to offer a naive collaborative distancing technique that leverages these features. Although the technique provides distance information, it does not imply the disclosure of the user’s locations being intrinsically prone to protect sensitive user data. A statistical error model is presented and validated through synthetic simulations and real, on-field experiments to support implementation in GNSS-equipped mobile devices. Accuracy and precision of IAR measurements are compared to other consolidated GNSS-based techniques showing comparable performance at lower complexity and computational effort

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

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 ultralow-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 GNSS-only CP among networked smartphones embedding ultra-low-cost 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

Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging

The implementation challenges of cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging are discussed and work on the subject is reviewed. System architecture and sensor fusion are identified as key challenges. A partially decentralized system architecture based on step-wise inertial navigation and step-wise dead reckoning is presented. This architecture is argued to reduce the computational cost and required communication bandwidth by around two orders of magnitude while only giving negligible information loss in comparison with a naive centralized implementation. This makes a joint global state estimation feasible for up to a platoon-sized group of agents. Furthermore, robust and low-cost sensor fusion for the considered setup, based on state space transformation and marginalization, is presented. The transformation and marginalization are used to give the necessary flexibility for presented sampling based updates for the inter-agent ranging and ranging free fusion of the two feet of an individual agent. Finally, characteristics of the suggested implementation are demonstrated with simulations and a real-time system implementation.Comment: 14 page

GNSS-only Collaborative Positioning Among Connected Vehicles

Cooperative positioning is considered a key strategy for the improvement of localization and navigation performance in harsh contexts such as urban areas. Modern communication paradigms can support the exchange of inter-vehicle ranges measured from on-board sensors or obtained through Global Satellite Navigation System (GNSS) measurements. The paper presents an overview of the GNSS-only collaborative localization in the context of cooperative connected cars. It provides an experimental example along with new results about the tight integration of collaboratively-generated inter-vehicle relative measurements collected by a target vehicle by means of a double differentiation w.r.t. to a set of five aiding vehicles. An average improvement of the positioning accuracy of about 11% motivates the research effort towards multi-agent connected positioning systems

GNSS Anti-Spoofing Defense Based on Cooperative Positioning

Radio navigation is of utmost importance in several application fields. Nowadays, many civil and professional applications massively rely on the Global Navigation Satellite System (GNSS) and related technologies to accurately estimate position and time. Existing GNSS-based systems are threatened by malicious attacks among which spoofing and meaconing constitute severe challenges to the receiver. Several of such GNSS systems constitute mass market applications and devices, and a threat to the GNSS receiver could have cascading effects at application levels and for interconnected systems. Networked GNSS receivers are in general ubiquitous because any receiver embedded in a complex system such as a smart device or smart connected cars can exploit network connectivity. This novel generation of valuable-performance GNSS receivers are prone both to standard RF spoofing attacks and to cyber-attacks conceived to hijack complex network based services such as DGNSS-based cooperative positioning. By means of a set of experimental tests, this paper highlights possible metrics to be checked to identify malicious attacks to the positioning and navigation systems in mass market connected devices. The network-based exchange of GNSS data such as GNSS raw measurements recently disclosed in Android smart devices is conceived in this work to offer the possibility to compare or combine such metrics to better identifies spoofing and meaconing attacks

Adaptive Bayesian State Estimation Integrating Non-stationary DGNSS Inter-Agent Distances

Bayesian navigation filters are broadly exploited in precise state estimation for kinematic applications such as vehicular positioning and navigation. Among these, Particle Filter (PF) has been shown as a valuable solution to support hybrid positioning algorithms such as sensor fusion to Global Navigation Satellite System (GNSS) and Cooperative Positioning (CP). Despite of an increased computational complexity w.r.t. conventional Kalman Filters (KFs), an effective weighting of the input measurements generally provides an improved accuracy of the output estimate. In the framework of the Differential GNSS (DGNSS) CP, this work presents an algorithm for the automated selection of the most appropriate error models for the tight-integration of non-stationary Differential GNSS (DGNSS) collaborative inter-agent distances. A model switching technique named Automated Adaptive Likelihood Switch (AALS) is proposed for a Cognitive Particle Filter (C-PF) architecture, based on the real-time approximation of the statistics of the inter-agent distances errors. The results achieved through realistic simulations demonstrated the effectiveness of the proposed solution in terms of error model selection. Therefore, an improvement of the position estimation accuracy was observed, since the cases in which DGNSS-CP would degrade performance due to possible mismodelling of the selected likelihood function are avoided

S3E: A Large-scale Multimodal Dataset for Collaborative SLAM

With the advanced request to employ a team of robots to perform a task collaboratively, the research community has become increasingly interested in collaborative simultaneous localization and mapping. Unfortunately, existing datasets are limited in the scale and variation of the collaborative trajectories, even though generalization between inter-trajectories among different agents is crucial to the overall viability of collaborative tasks. To help align the research community's contributions with realistic multiagent ordinated SLAM problems, we propose S3E, a large-scale multimodal dataset captured by a fleet of unmanned ground vehicles along four designed collaborative trajectory paradigms. S3E consists of 7 outdoor and 5 indoor sequences that each exceed 200 seconds, consisting of well temporal synchronized and spatial calibrated high-frequency IMU, high-quality stereo camera, and 360 degree LiDAR data. Crucially, our effort exceeds previous attempts regarding dataset size, scene variability, and complexity. It has 4x as much average recording time as the pioneering EuRoC dataset. We also provide careful dataset analysis as well as baselines for collaborative SLAM and single counterparts. Data and more up-to-date details are found at https://github.com/PengYu-Team/S3E

D2D-based Cooperative Positioning Paradigm for Future Wireless Systems: A Survey

Emerging communication network applications require a location accuracy of less than 1m in more than 95% of the service area. For this purpose, 5G New Radio (NR) technology is designed to facilitate high-accuracy continuous localization. In 5G systems, the existence of high-density small cells and the possibility of the device-to-device (D2D) communication between mobile terminals paves the way for cooperative positioning applications. From the standardization perspective, D2D technology is already under consideration (5G NR Release 16) for ultra-dense networks enabling cooperative positioning and is expected to achieve the ubiquitous positioning of below one-meter accuracy, thereby fulfilling the 5G requirements. In this survey, the strengths and weaknesses of D2D as an enabling technology for cooperative cellular positioning are analyzed (including two D2D approaches to perform cooperative positioning); lessons learned and open issues are highlighted to serve as guidelines for future research