A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT

Cooperative Positioning using Massive Differentiation of GNSS Pseudorange Measurements

With Differential GNSS (DGNSS), Single Differentiation (SD) of GNSS pseudorange mea- surements is computed with the aim of correcting harmful errors such as ionospheric and tropospheric delays. These errors can be mitigated to up to very few centimeters, which denotes a performance improvement with respect to the Standard Point Positioning (SPP) solution, widely used in GNSS receivers. However, with DGNSS it is necessary to have a very precise knowledge of the coordinates of a reference station in order to experience this performance improvement. We propose the Massive User-Centric Single Differentiation (MUCSD) algorithm, which is proven to have a comparable performance to DGNSS with- out the need of a reference station. Instead, N cooperative receivers which provide noisy observations of their position and clock bias are introduced in the model. The MUCSD algorithm is mathematically derived with an Iterative Weighted Least Squares (WLS) Estimator. The estimator lower bound is calculated with the Cramér-Rao Bound (CRB). Several scenarios are simulated to test the MUCSD algorithm with the MassiveCoop-Sim simulator. Results show that if the observations provided by the cooperative users have a noise of up to 10 meters, DGNSS performance can be obtained with N = 10. When observations are very noisy, the MUCSD performance still approaches DGNSS for high values of N

Impact of Positioning Technology on Human Navigation

In navigation from one place to another, spatial knowledge helps us establish a destination and route while travelling. Therefore, sufficient spatial knowledge is a vital element in successful navigation. To build adequate spatial knowledge, various forms of spatial tools have been introduced to deliver spatial information without direct experience (maps, descriptions, pictures, etc.). An innovation developed in the 1970s and available on many handheld platforms from the early 2000s is the Global Position System (GPS) and related map and text-based navigation support systems. Contemporary technical achievements, such as GPS, have made navigation more effective, efficient, and comfortable in most outdoor environments. Because GPS delivers such accurate information, human navigation can be supported without specific spatial knowledge. Unfortunately, there is no universal and accurate navigation system for indoor environments. Since smartphones have become increasingly popular, we can more frequently and easily access various positioning services that appear to work both indoors and outdoors. The expansion of positioning services and related navigation technology have changed the nature of navigation. For example, routes to destination are progressively determined by a “system,” not the individual. Unfortunately we only have a partial and nascent notion of how such an intervention affects spatial behaviour. The practical purpose of this research is to develop a trustworthy positioning system that functions in indoor environments and identify those aspects those should be considered before deploying Indoor Positioning System (IPS), all towards the goal of maintaining affordable positioning accuracy, quality, and consistency. In the same way that GPS provides worry free directions and navigation support, an IPS would extend such opportunities to many of our built environments. Unfortunately, just as we know little about how GPS, or any real time navigation system, affects human navigation, there is little evidence suggesting how such a system (indoors or outdoors) changes how we find our way. For this reason, in addition to specifying an indoor position system, this research examines the difference in human’s spatial behaviour based on the availability of a navigation system and evaluates the impact of varying the levels of availability of such tools (not available, partially available, or full availability). This research relies on outdoor GPS, but when such systems are available indoors and meet the accuracy and reliability or GPS, the results will be generalizable to such situations

Geographic Centroid Routing for Vehicular Networks

A number of geolocation-based Delay Tolerant Networking (DTN) routing protocols have been shown to perform well in selected simulation and mobility scenarios. However, the suitability of these mechanisms for vehicular networks utilizing widely-available inexpensive Global Positioning System (GPS) hardware has not been evaluated. We propose a novel geolocation-based routing primitive (Centroid Routing) that is resilient to the measurement errors commonly present in low-cost GPS devices. Using this notion of Centroids, we construct two novel routing protocols and evaluate their performance with respect to positional errors as well as traditional DTN routing metrics. We show that they outperform existing approaches by a significant margin.Comment: 6 page

Robust Positioning in the Presence of Multipath and NLOS GNSS Signals

GNSS signals can be blocked and reflected by nearby objects, such as buildings, walls, and vehicles. They can also be reflected by the ground and by water. These effects are the dominant source of GNSS positioning errors in dense urban environments, though they can have an impact almost anywhere. Non- line-of-sight (NLOS) reception occurs when the direct path from the transmitter to the receiver is blocked and signals are received only via a reflected path. Multipath interference occurs, as the name suggests, when a signal is received via multiple paths. This can be via the direct path and one or more reflected paths, or it can be via multiple reflected paths. As their error characteristics are different, NLOS and multipath interference typically require different mitigation techniques, though some techniques are applicable to both. Antenna design and advanced receiver signal processing techniques can substantially reduce multipath errors. Unless an antenna array is used, NLOS reception has to be detected using the receiver's ranging and carrier-power-to-noise-density ratio (C/N0) measurements and mitigated within the positioning algorithm. Some NLOS mitigation techniques can also be used to combat severe multipath interference. Multipath interference, but not NLOS reception, can also be mitigated by comparing or combining code and carrier measurements, comparing ranging and C/N0 measurements from signals on different frequencies, and analyzing the time evolution of the ranging and C/N0 measurements

Advanced Integration of GNSS and External Sensors for Autonomous Mobility Applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Pseudolite Architecture and Performance Analysis for the FAA\u27s NextGen Airspace

By 2025 the FAA plans to have fully implemented its NextGen Airspace design. NextGen takes advantage of modern positioning technologies as well as automation, data sharing, and display technologies that will allow more efficient use of our ever busier National Airspace (NAS). A key element of NextGen is the transition from surveillance RADAR providing aircraft separation and navigation to the use of the GPS and Automatic Dependent Surveillance Broadcast (ADS-B). ADS-B couples the precision of the GPS with networked ground and airborne receivers to provide precise situational awareness to pilots and controllers. The result is increased safety, capacity, and access with reduced reliance on an outdated and costly existing infrastructure. Reliance on the vulnerable GPS requires a backup system with higher positioning accuracy than those that are in place today. The USAF 746th Test Squadron at Holloman AFB, in partnership with Locata Corp., has demonstrated an Ultra High Accuracy Reference System (UHARS) over the Holloman Range composed of pseudolites (ground based satellites) transmitting GPS like signals. This study evaluates the suitability of the UHARS when applied on a national scale to meet Alternate Precision Navigation and Timing (APNT) requirements. From a systems architecture perspective UHARS is evaluated against APNT CONOPs stated Operational Improvements and Scenarios. From a signal architecture perspective the UHARS is evaluated against frequency and bandwidth constraints, service volume requirements and positioning accuracy determined by NextGen Airspace aircraft separation criteria

Entwicklung und Implementierung eines Peer-to-Peer Kalman Filters für Fußgänger- und Indoor-Navigation

Smartphones are an integral part of our society by now. They are used for messaging, searching the Internet, working on documents, and of course for navigation. Although smartphones are also used for car navigation their main area of application is pedestrian navigation. Almost all smartphones sold today comprise a GPS L1 receiver which provides position computation with accuracy between 1 and 10 m as long as the environment in beneficial, i.e. the line-of-sight to satellites is not obstructed by trees or high buildings. But this is often the case in areas where smartphones are used primarily for navigation. Users walk in narrow streets with high density, in city centers, enter, and leave buildings and the smartphone is not able to follow their movement because it loses satellite signals. The approach presented in this thesis addresses the problem to enable seamless navigation for the user independently of the current environment and based on cooperative positioning and inertial navigation. It is intended to realize location-based services in areas and buildings with limited or no access to satellite data and a large amount of users like e.g. shopping malls, city centers, airports, railway stations and similar environments. The idea of this concept was for a start based on cooperative positioning between users’ devices denoted here as peers moving within an area with only limited access to satellite signals at certain places (windows, doors) or no access at all. The devices are therefore not able to provide a position by means of satellite signals. Instead of deploying solutions based on infrastructure, surveying, and centralized computations like range measurements, individual signal strength, and similar approaches a decentralized concept was developed. This concept suggests that the smartphone automatically detects if no satellite signals are available and uses its already integrated inertial sensors like magnetic field sensor, accelerometer, and gyroscope for seamless navigation. Since the quality of those sensors is very low the accuracy of the position estimation decreases with each step of the user. To avoid a continuously growing bias between real position and estimated position an update has to be performed to stabilize the position estimate. This update is either provided by the computation of a position based on satellite signals or if signals are not available by the exchange of position data with another peer in the near vicinity using peer-to-peer ad-hoc networks. The received and the own position are processed in a Kalman Filter algorithm and the result is then used as new position estimate and new start position for further navigation based on inertial sensors. The here presented concept is therefore denoted as Peer-to-Peer Kalman Filter (P2PKF)

Seamless Outdoors-Indoors Localization Solutions on Smartphones: Implementation and Challenges

© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in http://doi.org/10.1145/2871166[EN] The demand for more sophisticated Location-Based Services (LBS) in terms of applications variety and accuracy is tripling every year since the emergence of the smartphone a few years ago. Equally, smartphone manufacturers are mounting several wireless communication and localization technologies, inertial sensors as well as powerful processing capability, to cater to such LBS applications. A hybrid of wireless technologies is needed to provide seamless localization solutions and to improve accuracy, to reduce time to fix, and to reduce power consumption. The review of localization techniques/technologies of this emerging field is therefore important. This article reviews the recent research-oriented and commercial localization solutions on smartphones. The focus of this article is on the implementation challenges associated with utilizing these positioning solutions on Android-based smartphones. Furthermore, the taxonomy of smartphone-location techniques is highlighted with a special focus on the detail of each technique and its hybridization. The article compares the indoor localization techniques based on accuracy, utilized wireless technology, overhead, and localization technique used. The pursuit of achieving ubiquitous localization outdoors and indoors for critical LBS applications such as security and safety shall dominate future research efforts.This research was sponsored by Koya University, Kurdistan Region-Iraq. The authors also would like to thank Dr. Ali Al-Sherbaz (from the University of Northampton-UK) and Dr. Naseer Al-Jawad (from the University of Buckingham-UK) for providing and improving the quality of this article in terms of academic and technical writing.Maghdid, HS.; Lami, IA.; Ghafoor, KZ.; Lloret, J. (2016). 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