RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained

Sisäpaikannus: Teknologiat ja käyttötapaukset vähittäiskaupan alalla

Indoor positioning systems (IPS) are required in buildings to offer the possibility to position people and assets indoors, as the widely utilized GPS signal cannot penetrate through walls. IPSs are already implemented in many indoor environments. Several indoor positioning technologies exist, but none of them is clearly a dominant technology over the others. Consequently, this study identifies the different kinds of indoor positioning technologies and methods as well as the use cases they are used in. For this purpose, six companies using or developing indoor positioning systems were interviewed. The interviews were held in person, and they were 60-minute long semi-structured interviews with a set of questions in Appendix 1. In addition, two companies interested in indoor positioning, and that are working with retail were interviewed in 30-minute semi-structured interviews with questions in Appendix 2. Indoor positioning is employed in the interviewed companies to help users to navigate in public spaces; raise employee satisfaction in an office; improve customer service and satisfaction in malls, stores, and restaurants and develop processes and safety in warehouses. These different use cases have distinctive specifications and needs for indoor positioning, and thus, there is not a simple solution as to which technology is the right choice for a particular use case. Nevertheless, three points affecting the choice of indoor positioning technology were concluded from the interviews: 1) the accuracy of a technology, 2) whether the positioning happens through a tag or a mobile device, and 3) if positioning infrastructure, such as anchor nodes, can be installed in the building. Finally, based on the interviews, a suggested model for an indoor positioning system for a retail company is presented in a form of a Value Network Configuration.Sisäpaikannusjärjestelmiä tarvitaan rakennuksissa, jotta ihmisiä ja tavaroita voidaan paikantaa sisätiloissa, sillä ulkona yleisesti käytetty GPS signaali ei pysty läpäisemään rakennusten seiniä. Vaikka sisäpaikannusta käytetäänkin jo useissa eri sisätiloissa ja useita eri sisäpaikannusteknologioita on olemassa, mikään niistä ei ole selvästi hallitseva teknologia. Tässä tutkimuksessa tunnistetaan eri sisäpaikannusteknologiat ja –tekniikat kuten myös niitä hyödyntävät käyttötapaukset. Tätä varten haastateltiin kuutta eri yritystä, jotka käyttävät tai tarjoavat sisäpaikannusjärjestelmiä. Haastattelut olivat puolistrukturoituja, kestivät 60 minuuttia ja ne pidettiin kasvotusten. Lisäksi haastateltiin 30 minuutin puolistrukturoiduissa haastatteluissa kahta kaupan alaan liittyvää yritystä, jotka ovat kiinnostuneita sisäpaikannuksesta. Haastattelukysymykset ovat liitteissä 1 ja 2. Sisäpaikannusta käytetään haastatelluissa yrityksissä käyttäjien navigoinnin helpottamiseksi julkisissa tiloissa, työntekijöiden tyytyväisyyden kasvattamiseen toimistossa, asiakaspalvelun ja asiakkaiden tyytyväisyyden parantamiseen ostoskeskuksissa, kaupoissa ja ravintoloissa sekä prosessien ja turvallisuuden kehittämiseen varastoissa. Näillä eri käyttötapauksilla on hyvin erilaiset vaatimukset ja tarpeet sisäpaikannukselle, joten ei ole olemassa vain yhtä hyvää teknologista ratkaisua tietylle käyttötapaukselle. Haastatteluista oli kuitenkin mahdollista muodostaa kolme sisäpaikannusteknologian valintaan vaikuttavaa asiaa: 1) sisäpaikannusteknologian tarkkuus, 2) tapahtuuko paikannus mobiililaitteen vai käyttäjän kantaman tunnisteen kautta ja 3) voiko paikannusjärjestelmän tukiasemia asentaa rakennukseen. Lopuksi esitellään ehdotelma sisäpaikannusmallista arvoverkkokonfiguraatiolla (Value Network Configuration) vähittäiskaupan alan yritykselle haastatteluiden perusteella

Advanced real-time indoor tracking based on the Viterbi algorithm and semantic data

A real-time indoor tracking system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy, that is, the environment of the object that is being tracked and a motion model. The starting point is a fingerprinting technique for which an advanced network planner is used to automatically construct the radio map, avoiding a time consuming measurement campaign. The developed algorithm was verified with simulations and with experiments in a building-wide testbed for sensor experiments, where a median accuracy below 2 m was obtained. Compared to a reference algorithm without Viterbi or semantic data, the results indicated a significant improvement: the mean accuracy and standard deviation improved by, respectively, 26.1% and 65.3%. Thereafter a sensitivity analysis was conducted to estimate the influence of node density, grid size, memory usage, and semantic data on the performance

Indoor Localization Solutions for a Marine Industry Augmented Reality Tool

In this report are described means for indoor localization in special, challenging circum-stances in marine industry. The work has been carried out in MARIN project, where a tool based on mobile augmented reality technologies for marine industry is developed. The tool can be used for various inspection and documentation tasks and it is aimed for improving the efficiency in design and construction work by offering the possibility to visualize the newest 3D-CAD model in real environment. Indoor localization is needed to support the system in initialization of the accurate camera pose calculation and auto-matically finding the right location in the 3D-CAD model. The suitability of each indoor localization method to the specific environment and circumstances is evaluated.Siirretty Doriast

Positioning in Indoor Mobile Systems

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RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

WiFiPoz -- an accurate indoor positioning system

Location based services are becoming an important part of life. Wide adoption of GPS in mobile devices combined with cellular networks has practically solved the problem of outdoor localization needs. The problem of locating an indoor user has being studied only recently. Much research contributed to the innovative concept of an indoor positioning system. By analyzing different technologies and algorithms, this thesis concluded that, considering a trade-off between accuracy and cost, a Wi-Fi based Fingerprint method is proved to be the most promising approach to determine the location of a mobile device. However, the Fingerprint method works in two phases-an offline training phase (collection of Received Signal Strength signatures) and an online phase in which data from the first phase is used to determine the current position of a mobile user. The number of training points in a certain area has a direct impact on the accuracy of the system. As a result, the offline phase is a tedious and cumbersome process and the positioning systems are only as accurate as the offline training phase has been detailed. Moreover, the offline phase must be repeated every time a change in the environment occurs. To avoid these limitations, we focus on improving the accuracy of the indoor positioning system, without increasing the number of training points. This thesis presents a Wi-Fi based system for locating a user inside a building. The system is named WiFiPoz, which means Wi-Fi positioning system based on the zoning method. WiFiPoz has a novel approach to Fingerprint method that incorporates Propagation and zoning methods. Experimental results show that WiFiPoz is highly efficient both in accuracy and costs. Compared to traditional Fingerprint methods, with the optimization of the accuracy of the location estimation, WiFiPoz reduces the number of training points. This feature makes it possible to quickly adapt to changes in the environment. In order to explore another possible solution, this thesis also developed, implemented and tested an indoor positioning system named GIS (Geometric Information based positioning System), which is based on a model proposed by another researcher. Several experiments were run in the offline phase and results were compared between the traditional Fingerprint method, GIS and proposed WiFiPoz. We concluded that WiFiPoz is a more efficient and simple way to increase the accuracy of the location determination with fewer training points --Document

Modelling of Indoor Positioning Systems Based on Location Fingerprinting

In recent years, localization systems for indoor vicinity using the present wireless local area (WLAN) network infrastructure have been proposed. Such positioning systems create the usage of location fingerprinting instead of direction or time of arrival techniques for deciding the location of mobile users. However experimental study associated to such localization systems have been proposed, high attenuation and signal scattering related to greater density of wall attenuation still affecting the indoor positioning performance. This paper presents an analytical model for minimizing high signal attenuation effect for WLAN fingerprinting indoor positioning systems. The model employs the probabilistic algorithm that using signal relation method