Positioning Accuracy Improvement via Distributed Location Estimate in Cooperative Vehicular Networks

The development of cooperative vehicle safety (CVS) applications, such as collision warnings, turning assistants, and speed advisories, etc., has received great attention in the past few years. Accurate vehicular localization is essential to enable these applications. In this study, motivated by the proliferation of the Global Positioning System (GPS) devices, and the increasing sophistication of wireless communication technologies in vehicular networks, we propose a distributed location estimate algorithm to improve the positioning accuracy via cooperative inter-vehicle distance measurement. In particular, we compute the inter-vehicle distance based on raw GPS pseudorange measurements, instead of depending on traditional radio-based ranging techniques, which usually either suffer from high hardware cost or have inadequate positioning accuracy. In addition, we improve the estimation of the vehicles' locations only based on the inaccurate GPS fixes, without using any anchors with known exact locations. The algorithm is decentralized, which enhances its practicability in highly dynamic vehicular networks. We have developed a simulation model to evaluate the performance of the proposed algorithm, and the results demonstrate that the algorithm can significantly improve the positioning accuracy.Comment: To appear in Proc. of the 15th International IEEE Conference on Intelligent Transportation Systems (IEEE ITSC'12

Technologies and solutions for location-based services in smart cities: past, present, and future

Location-based services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS rely on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This paper elaborates upon the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas

Infrastructure Wi-Fi for connected autonomous vehicle positioning : a review of the state-of-the-art

In order to realize intelligent vehicular transport networks and self driving cars, connected autonomous vehicles (CAVs) are required to be able to estimate their position to the nearest centimeter. Traditional positioning in CAVs is realized by using a global navigation satellite system (GNSS) such as global positioning system (GPS) or by fusing weighted location parameters from a GNSS with an inertial navigation systems (INSs). In urban environments where Wi-Fi coverage is ubiquitous and GNSS signals experience signal blockage, multipath or non line-of-sight (NLOS) propagation, enterprise or carrier-grade Wi-Fi networks can be opportunistically used for localization or “fused” with GNSS to improve the localization accuracy and precision. While GNSS-free localization systems are in the literature, a survey of vehicle localization from the perspective of a Wi-Fi anchor/infrastructure is limited. Consequently, this review seeks to investigate recent technological advances relating to positioning techniques between an ego vehicle and a vehicular network infrastructure. Also discussed in this paper is an analysis of the location accuracy, complexity and applicability of surveyed literature with respect to intelligent transportation system requirements for CAVs. It is envisaged that hybrid vehicular localization systems will enable pervasive localization services for CAVs as they travel through urban canyons, dense foliage or multi-story car parks

A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

WiFi-based PCL for monitoring private airfields

In this article, the potential exploitation of WiFi-based PCL systems is investigated with reference to a real-world civil application in which these sensors are expected to nicely complement the existing technologies adopted for monitoring purposes, especially when operating against noncooperative targets. In particular, we consider the monitoring application of small private airstrips or airfields. With this terminology, we refer to open areas designated for the takeoff and landing of small aircrafts that, unlike an airport, have generally short and possibly unpaved runways (e.g., grass, dirt, sand, or gravel surfaces) and do not necessarily have terminals. More important, such areas usually are devoid of conventional technologies, equipment, or procedures adopted to guarantee safety and security in large aerodromes.There exist a huge number of small, privately owned, and unlicensed airfields around the world. Private aircraft owners mainly use these “airports” for recreational, single-person, or private flights for small groups and training flight purposes. In addition, residential airparks have proliferated in recent years, especially inthe United States, Canada, and South Africa. A residential airpark, or “fly-in community,” features common airstrips where homes with attached hangars allow owners to taxi from their hangar to a shared runway. In many cases, roads are dual use for both cars and planes.In such scenarios, the possibility to employ low-cost, compact, nonintrusive, and nontransmitting sensors as a way to improve safety and security with limited impact on the airstrips' users would be of great potential interest. To this purpose, WiFi-based passive radar sensors appear to be good candidates [23]. Therefore, we investigate their application against typical operative conditions experienced in the scenarios described earlier. The aim is to assess the capability to detect, localize, and track authorized and unauthorized targets that can be occupying the runway and the surrounding areas