Real-time localization using received signal strength

Locating and tracking assets in an indoor environment is a fundamental requirement for several applications which include for instance network enabled manufacturing. However, translating time of flight-based GPS technique for indoor solutions has proven very costly and inaccurate primarily due to the need for high resolution clocks and the non-availability of reliable line of sight condition between the transmitter and receiver. In this dissertation, localization and tracking of wireless devices using radio signal strength (RSS) measurements in an indoor environment is undertaken. This dissertation is presented in the form of five papers. The first two papers deal with localization and placement of receivers using a range-based method where the Friis transmission equation is used to relate the variation of the power with radial distance separation between the transmitter and receiver. The third paper introduces the cross correlation based localization methodology. Additionally, this paper also presents localization of passive RFID tags operating at 13.56MHz frequency or less by measuring the cross-correlation in multipath noise from the backscattered signals. The fourth paper extends the cross-correlation based localization algorithm to wireless devices operating at 2.4GHz by exploiting shadow fading cross-correlation. The final paper explores the placement of receivers in the target environment to ensure certain level of localization accuracy under cross-correlation based method. The effectiveness of our localization methodology is demonstrated experimentally by using IEEE 802.15.4 radios operating in fading noise rich environment such as an indoor mall and in a laboratory facility of Missouri University of Science and Technology. Analytical performance guarantees are also included for these methods in the dissertation --Abstract, page iv

Design and realization of precise indoor localization mechanism for Wi-Fi devices

Despite the abundant literature in the field, there is still the need to find a time-efficient, highly accurate, easy to deploy and robust localization algorithm for real use. The algorithm only involves minimal human intervention. We propose an enhanced Received Signal Strength Indicator (RSSI) based positioning algorithm for Wi-Fi capable devices, called the Dynamic Weighted Evolution for Location Tracking (DWELT). Due to the multiple phenomena affecting the propagation of radio signals, RSSI measurements show fluctuations that hinder the utilization of straightforward positioning mechanisms from widely known propagation loss models. Instead, DWELT uses data processing of raw RSSI values and applies a weighted posterior-probabilistic evolution for quick convergence of localization and tracking. In this paper, we present the first implementation of DWELT, intended for 1D location (applicable to tunnels or corridors), and the first step towards a more generic implementation. Simulations and experiments show an accuracy of 1m in more than 81% of the cases, and less than 2m in the 95%.Peer ReviewedPostprint (published version

Effiziente Lokalisierung von Nutzern und Geräten in Smarten Umgebungen

The thesis considers determination of location of sensors and users in smart environments using measurements of Received Signal Strength (RSS). The first part of the thesis focuses on localization in Wireless Sensor Networks and contributes two fully distributed algorithms which address the Sensor Selection Problem and provide the best trade-off between energy consumption and localization accuracy among the algorithms considered. Furthermore, the thesis contributes to Device Free Localization an indoor localization concept providing scalable and highly accurate location estimates (prototype: 0.36m² MSE) while using a COTS passive RFID-System and not relying on user-carried sensors

Survey of energy efficient tracking and localization techniques in buildings using optical and wireless communication media

This paper presents a survey of beamforming, beamsteering and mobile tracking techniques. The survey was made in the context of the SOWICI project. The aim of this project is to reduce power consumption of data exchanging devices within houses. An optical fiber network is used for data transport to and from rooms whereas wireless transceivers communicate with appliances within the rooms. Using this approach, the aim is to reduce power consumption and exposure to electromagnetic radiation. To realize this, beamforming will be used to only radiate energy in, and receive signals from, the direction of interest. Because appliances within households can move, some of them even relatively fast, the pointing direction of the beam should be steerable. The pointing direction can be deduced from the communication link (beamsteering) or via separate mobile tracking techniques

Puolivalvottu WLAN-radiokarttojen oppiminen

In this thesis a manifold learning method is applied to the problem of WLAN positioning and automatic radio map creation. Due to the nature of WLAN signal strength measurements, a signal map created from raw measurements results in non-linear distance relations between measurement points. These signal strength vectors reside in a high-dimensioned coordinate system. With the help of the so called Isomap-algorithm the dimensionality of this map can be reduced, and thus more easily processed. By embedding position-labeled strategic key points, we can automatically adjust the mapping to match the surveyed environment. The environment is thus learned in a semi-supervised way; gathering training points and embedding them in a two-dimensional manifold gives us a rough mapping of the measured environment. After a calibration phase, where the labeled key points in the training data are used to associate coordinates in the manifold representation with geographical locations, we can perform positioning using the adjusted map. This can be achieved through a traditional supervised learning process, which in our case is a simple nearest neighbors matching of a sampled signal strength vector. We deployed this system in two locations in the Kumpula campus in Helsinki, Finland. Results indicate that positioning based on the learned radio map can achieve good accuracy, especially in hallways or other areas in the environment where the WLAN signal is constrained by obstacles such as walls.Työssä sovelletaan monisto-oppimismenetelmää WLAN-paikannuksen ja automaattisen radiokartan luonnin ongelmaan. WLAN-signaalivoimakkuuksien mittausten luonteen takia käsittelemättömät mittaukset aiheuttavat epälineaarisia suhteita radiokartan mittauspisteiden välille. Nämä signaalivoimakkuusvektorit sijaitsevat avaruudessa jolla on korkea ulottuvuus. Niin kutsutun Isomap-algoritmin avulla kartan ulottuvuuksia voidaan karsia, jolloin sitä on helpompi työstää. Upottamalla karttaan merkittyjä avainpisteitä, se voidaan automaattisesti säätää vastaamaan mitattua ympäristöä. Ympäristö siis opitaan puolivalvotusti; keräämällä harjoituspisteitä ja upottamalla ne kaksiulotteiseen monistoon saadaan karkea kartta ympäristöstä. Kalibrointivaiheen jälkeen, jossa merkittyjä avainpisteitä käytetään yhdistämään moniston koordinaatit maantieteellisiin kohteisiin, voidaan suorittaa paikannusta säädetyn kartan avulla. Tämä voidaan tehdä perinteisen valvotun oppimisen avulla, joka tässä tapauksessa on yksinkertainen lähimmän naapurin löytäminen mitatulle signaalivoimakkuusvektorille. Järjestelmää kokeiltiin kahdessa paikassa Kumpulan kampuksessa Helsingissä. Tulokset viittaavat siihen että opitun radiokartan avulla paikannus voi saavuttaa hyvän tarkkuuden, etenkin käytävissä ja muissa tiloissa jossa esteet kuten seinät rajoittavat WLAN-signaalia

Auto-localization algorithm for local positioning systems

This paper studies the problem of determining the position of beacon nodes in Local Positioning Systems (LPSs), for which there are no inter-beacon distance measurements available and neither the mobile node nor any of the stationary nodes have positioning or odometry information. The common solution is implemented using a mobile node capable of measuring its distance to the stationary beacon nodes within a sensing radius. Many authors have implemented heuristic methods based on optimization algorithms to solve the problem. However, such methods require a good initial estimation of the node positions in order to find the correct solution. In this paper we present a new method to calculate the inter-beacon distances, and hence the beacons positions, based in the linearization of the trilateration equations into a closed-form solution which does not require any approximate initial estimation. The simulations and field evaluations show a good estimation of the beacon node positions