DVB-T Positioning with a One Shot Receiver

In this paper a one shot receiver for DVB-T positioning is presented. DVB-T SFN signals can be used as Signals-of-Opportunity in urban environment to assist GNSS in case the GNSS-only positioning shows degraded performance. The normal mechanism of DVB-T positioning involves a tracking stage to refine the coarse delay estimation obtained by the acquisition stage. However due to the high SNR of DVB-T signals, the delay estimation can be refined by some simple interpolation methods with lower complexity and power consumption. Two different interpolation methods, linear interpolation and sinc interpolation, are analysed in the paper. Simulation results show that the one shot receiver proposed in this paper behaves as a tracking-based receiver, but exhibits a lower complexit

Human Behavior Cognition Using Smartphone Sensors

Peer reviewe

Context-aware Peer-to-Peer and Cooperative Positioning

Peer-to-peer and cooperative positioning represent one of the major evolutions for mass-market positioning, bringing together capabilities of Satellite Navigation and Communication Systems. It is well known that smartphones already provide user position leveraging both GNSS and information collected through the communication network (e.g., Assisted-GNSS). However, exploiting the exchange of information among close users can attain further benefits. In this paper, we deal with such an approach and show that sharing information on the environmental conditions that characterize the reception of satellite signals can be effectively exploited to improve the accuracy and availability of user positioning. This approach extends the positioning service to indoor environments and, in general, to any scenario where full visibility of the satellite constellation cannot be grante

Robustness, Security and Privacy in Location-Based Services for Future IoT : A Survey

Internet of Things (IoT) connects sensing devices to the Internet for the purpose of exchanging information. Location information is one of the most crucial pieces of information required to achieve intelligent and context-aware IoT systems. Recently, positioning and localization functions have been realized in a large amount of IoT systems. However, security and privacy threats related to positioning in IoT have not been sufficiently addressed so far. In this paper, we survey solutions for improving the robustness, security, and privacy of location-based services in IoT systems. First, we provide an in-depth evaluation of the threats and solutions related to both global navigation satellite system (GNSS) and non-GNSS-based solutions. Second, we describe certain cryptographic solutions for security and privacy of positioning and location-based services in IoT. Finally, we discuss the state-of-the-art of policy regulations regarding security of positioning solutions and legal instruments to location data privacy in detail. This survey paper addresses a broad range of security and privacy aspects in IoT-based positioning and localization from both technical and legal points of view and aims to give insight and recommendations for future IoT systems providing more robust, secure, and privacy-preserving location-based services.Peer reviewe

Combining visual, pedestrian, and collaborative navigation techniques for team based infrastructure free indoor navigation

Peer reviewe

An Indoor Positioning System Based on Wearables for Ambient-Assisted Living

The urban population is growing at such a rate that by 2050 it is estimated that 84% of the world’s population will live in cities, with flats being the most common living place. Moreover, WiFi technology is present in most developed country urban areas, with a quick growth in developing countries. New Ambient-Assisted Living applications will be developed in the near future having user positioning as ground technology: elderly tele-care, energy consumption, security and the like are strongly based on indoor positioning information. We present an indoor positioning system for wearable devices based on WiFi fingerprinting. Smart-watch wearable devices are used to acquire the WiFi strength signals of the surrounding Wireless Access Points used to build an ensemble of Machine Learning classification algorithms. Once built, the ensemble algorithm is used to locate a user based on the WiFi strength signals provided by the wearable device. Experimental results for five different urban flats are reported, showing that the system is robust and reliable enough for locating a user at room level into his/her home. Another interesting characteristic of the presented system is that it does not require deployment of any infrastructure, and it is unobtrusive, the only device required for it to work is a smart-watch.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness through the “Proyectos I + D Excelencia” programme (TIN2015-70202-P) and the “Redes de Excelencia” programme (TEC2015-71426-REDT), and from the Regional Government of Valencia (‘Proyectos de I + D para Grupos de Investigación Emergentes’ GV/2016/159). Special thanks to Víctor, Maricarmen, Inma and Daniel who lent their houses for performing the experiments

Context Awareness for Navigation Applications

This thesis examines the topic of context awareness for navigation applications and asks the question, “What are the benefits and constraints of introducing context awareness in navigation?” Context awareness can be defined as a computer’s ability to understand the situation or context in which it is operating. In particular, we are interested in how context awareness can be used to understand the navigation needs of people using mobile computers, such as smartphones, but context awareness can also benefit other types of navigation users, such as maritime navigators. There are countless other potential applications of context awareness, but this thesis focuses on applications related to navigation. For example, if a smartphone-based navigation system can understand when a user is walking, driving a car, or riding a train, then it can adapt its navigation algorithms to improve positioning performance. We argue that the primary set of tools available for generating context awareness is machine learning. Machine learning is, in fact, a collection of many different algorithms and techniques for developing “computer systems that automatically improve their performance through experience” [1]. This thesis examines systematically the ability of existing algorithms from machine learning to endow computing systems with context awareness. Specifically, we apply machine learning techniques to tackle three different tasks related to context awareness and having applications in the field of navigation: (1) to recognize the activity of a smartphone user in an indoor office environment, (2) to recognize the mode of motion that a smartphone user is undergoing outdoors, and (3) to determine the optimal path of a ship traveling through ice-covered waters. The diversity of these tasks was chosen intentionally to demonstrate the breadth of problems encompassed by the topic of context awareness. During the course of studying context awareness, we adopted two conceptual “frameworks,” which we find useful for the purpose of solidifying the abstract concepts of context and context awareness. The first such framework is based strongly on the writings of a rhetorician from Hellenistic Greece, Hermagoras of Temnos, who defined seven elements of “circumstance”. We adopt these seven elements to describe contextual information. The second framework, which we dub the “context pyramid” describes the processing of raw sensor data into contextual information in terms of six different levels. At the top of the pyramid is “rich context”, where the information is expressed in prose, and the goal for the computer is to mimic the way that a human would describe a situation. We are still a long way off from computers being able to match a human’s ability to understand and describe context, but this thesis improves the state-of-the-art in context awareness for navigation applications. For some particular tasks, machine learning has succeeded in outperforming humans, and in the future there are likely to be tasks in navigation where computers outperform humans. One example might be the route optimization task described above. This is an example of a task where many different types of information must be fused in non-obvious ways, and it may be that computer algorithms can find better routes through ice-covered waters than even well-trained human navigators. This thesis provides only preliminary evidence of this possibility, and future work is needed to further develop the techniques outlined here. The same can be said of the other two navigation-related tasks examined in this thesis

Finnish permanent GNSS network FinnRef - evolution towards a versatile positioning service

The doctoral thesis is conducted in collaboration with Aalto University and Finnish Geospatial Research Institute.The National Land Survey of Finland maintains the FinnRef network of continuously operating GNSS reference stations (CORS). FinnRef is the basis of the EUREF-FIN reference frame in Finland. Continuous time series ensure an accurate link between global GNSS-based coordinates and the national reference frame. In a CORS network it is essential that coordinates and coordinate time series of the reference stations are up to date, accurate and free from biases. In this dissertation we introduce the development of FinnRef from a network of 13 GPS stations into a versatile modern positioning service. Both old and new FinnRef stations are explained and it is shown how a high quality CORS station should be established today. We were one of the first groups to show the annual periodicity of GNSS time series. In Finland, land uplift is of high importance since it changes the coordinates continuously. We compared our uplift rates to independent results (tide gauges, precise levelling, GPS results of the BIFROST group). Agreement was very good showing that GPS is a powerful tool for monitoring the land uplift in Finland. Using a baseline in Lithuania we tested an idea to validate GPS processing parameters against metrological ground truth. The length of the baseline is traceable to the definition of the metre with an uncertainty based on our calibrations. The test was successful and showed that for most accurate measurements individually calibrated antennas must be used.After the first renewal phase of FinnRef we showed that our national network of 20 stations can provide NRTK corrections of the same accuracy level as services having five times more stations. The challenge was the reliability of individual coordinate measurements but this can be overcome by proper use of repeated measurements. One of the most important results of this dissertation was that we showed the power of using metrological ground truth for validating GPS. The results of this dissertation will enable the creation of a dense GNSS based velocity field for intra-plate deformation models. This will improve the accuracy of transformations from measured GNSS coordinates to the national reference frame and make possible an accurate, reliable (semi)dynamic reference frame in Finland. It is also noteworthy that we showed how the FINPOS positioning service based on FinnRef data could give citizens direct access to the national EUREF-FIN reference frame. FinnRef could also be used as a backbone for GNSS corrections needed for intelligent traffic applications in Finland.Maanmittauslaitos kerää satelliittipaikannusjärjestelmien (GNSS) lähettämää dataa jatkuvasti rekisteröivien FinnRef-tukiasemien avulla. Maanlaajuinen FinnRef-verkko on Suomen EUREF-FIN -vertauskehyksen runko. Verkon keräämät pitkät aikasarjat mahdollistavat tarkkojen muunnosmallien johtamisen satelliittipaikannusjärjestelmien tuottamien maailmanlaajuisten koordinaattien ja kansallisen EUREF-FIN -järjestelmän välille. On oleellista, että pysyvien GNSS-asemien tuottamat koordinaatit ja koordinaattien aikasarjat ovat laadukkaita ja virheettömiä. Tässä väitöskirjassa esitetään Suomen pysyvän GNSS-verkon kehitys 13 aseman GPS-asemien verkosta kohti tiheämpää ja monipuolista paikannuspalvelua. Työssä esitellään alkuperäisen FinnRef-verkon ja uudistetun verkon rakenne, toimintaperiaatteet ja uusin tieto siitä kuinka asemat tulee tänä päivänä perustaa. Olimme yksi ensimmäisistä tutkimusryhmistä, jotka raportoivat GPS-aikasarjoissa selkeää vuotuista periodisuutta. Suomessa eräs koordinaatteja jatkuvasti muuttava ilmiö on maankohoaminen. Vertasimme omia GPS:llä saatuja maankohoamisarvoja riippumattomiin tuloksiin (toistetut tarkkavaaitukset, mareografihavainnot ja kansainvälisen BIFROST-tutkimusryhmän eri metodilla tekemä GPS-analyysi). Yhteensopivuus oli hyvä ja osoitti GPS:n olevan tehokas työkalu myös maankuoren pystyliikkeiden seurantaan. Testasimme Liettuassa olevalla perusviivalla mahdollisuutta validoida GPS-laskentaparametreja metrologisesti luotettavaan referenssimittaukseen. Mittauksen epävarmuusketju on tekemämme kalibroinnin perusteella jäljitettävissä metrin määritelmään. Testi osoittautui menestykselliseksi ja osoitimme, että tarkimpiin mahdollisiin mittauksiin jokainen GPS-antenni tulee kalibroida yksilönä. Ensimmäisen FinnRef-verkon uudistuksen jälkeen osoitimme, että valtakunnallisella 20 tukiaseman GNSS verkolla voidaan päästä samalle tarkkuustasolle kuin viisi kertaa enemmän asemia olevissa palveluissa. Haasteeksi tulee yksittäisten mittausten luotettavuus, joka voidaan ratkaista toistomittauksin. Väitöskirjan tärkeimpiä tuloksia oli osoittaa metrologisen lähestymistavan hyväksikäytön hyödyllisyys validoitaessa GNSS-laskentaparametreja. Tulokset mahdollistavat entistä tarkemman GNSS-tukiasemien dataan perustuvan maankuoren liikemallien luomisen, joka tarkentaa kansainvälisten ja kansallisen koordinaattijärjestelmien välistä muunnosta sekä aikariippuvan koordinaattijärjestelmän. Osoitimme myös, kuinka FinnRef-verkkoon perustuva FINPOS-paikannuspalvelu mahdollistaisi kansalaisille saumattoman pääsyn kansalliseen EUREF-FIN -järjestelmään. FinnRef-verkkoa voitaisiin hyödyntää myös runkoverkkona, joka voi osaltaan auttaa parantamaan älyliikenteen paikannuksen tarkkuutta

Context-aware Services for Mobile Devices: From Architecture Design to Empirical Inference

Currently, mobile devices are aware of user position, which can be provided to mobile apps for the development of tailored services known as Location-Based Services. Further advances on current Location-based Services (LBS), i.e. using any other information from the user such as gender, music preferences etc, may lead to transition from a Location-Based environment to a fully developed ContextAware environment.The current trend towards Context-aware Services (CAS) is reflected in academic research since more than twenty years as well as in the progress in Software Development Kits (SDKs) of the main mobile operating systems, where CAS frameworks are currently being used. However, there is no community agreement for modelling context CAS and little is known about the architecture of these context management frameworks of the mobile operating systems.Based on previous research in the area of CAS, I establish and analyse a reasoning architecture, the Context Engine (CE), that enables the main steps of designing and implementing context-aware services. The chief utility of CAS is their ability to formulate and encapsulate information, obtain user context through context acquisition tools and distribute it to third-party applications that build personalised services based on the provided information. The CE has the responsibility of selecting the optimal context acquisition tool to solve a concrete problem which is discussed in this dissertation.Furthermore, this thesis contributes to the development of context inference tools by studying two particular cases. The first case aims at inferring user (semantic) location information based on mobile phone usage data. This first case has been carried out in collaboration with Microsoft Finland, which provides a similar context inference solution to mobile developers through their Software Development Kit (SDK). The second case aims at inferring user information based on social network information, i.e. infer user information based on his or her connections. Both studies yield positive results and have the potential to be extended to obtain better context acquisition tools and, therefore, better user context

Design and Implementation of System Components for Radio Frequency Based Asset Tracking Devices to Enhance Location Based Services. Study of angle of arrival techniques, effects of mutual coupling, design of an angle of arrival algorithm, design of a novel miniature reconfigurable antenna optimised for wireless communication systems

The angle of arrival estimation of multiple sources plays a vital role in the field of array signal processing as MIMO systems can be employed at both the transmitter and the receiver end and the system capacity, reliability and throughput can be significantly increased by using array signal processing. Almost all applications require accurate direction of arrival (DOA) estimation to localize the sources of the signals. Another important parameter of localization systems is the array geometry and sensor design which can be application specific and is used to estimate the DOA. In this work, various array geometries and arrival estimation algorithms are studied and then a new scheme for multiple source estimation is proposed and evaluated based on the performance of subspace and non-subspace decomposition methods. The proposed scheme has shown to outperform the conventional Multiple Signal Classification (MUSIC) estimation and Bartlett estimation techniques. The new scheme has a better performance advantage at low and high signal to noise ratio values (SNRs). The research work also studies different array geometries for both single and multiple incident sources and proposes a geometry which is cost effective and efficient for 3, 4, and 5 antenna array elements. This research also considers the shape of the ground plane and its effects on the angle of arrival estimation and in addition it shows how the mutual couplings between the elements effect the overall estimation and how this error can be minimised by using a decoupling matrix. At the end, a novel miniaturised multi element reconfigurable antenna to represent the receiver base station is designed and tested. The antenna radiation patterns in the azimuth angle are almost omni-directional with linear polarisation. The antenna geometry is uniplanar printed logspiral with striplines feeding network and biased components to improve the impedance bandwidth. The antenna provides the benefit of small size, and re-configurability and is very well suited for the asset tracking applications