Thesis deal with geolocation in internet network. There are described possibilities of geolocation and thesis is mainly focused on passive geolocation methods. Under passive geolocation belongs location databases which there are described as in theoretical way as used in practical part of thesis. In practical part there is created complex system for geolocation in internet environment which used paid and free geolocation databases. Another used database is WHOIS. Data from paid databases is processed and accuracy of databases is evaluated.

ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California / Brigham Young University recently introduced a project for undergraduates in which a miniature unmanned aerial vehicle system is constructed. The system is capable of autonomous flight, takeoff, landing, and navigation through a planned path. In addition, through the use of video and telemetry collected by the vehicle, accurate geolocation of specified targets is performed. This paper outlines our approach and successes in facilitating this accomplishment at the undergraduate level.

International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California / This paper presents an alternative approach to high-precision bearing estimation for navigation and guidance in homing and docking of underwater vehicles. This new technique is significantly simpler than the conventional methods in terms of computation complexity and yet produces results of superior precision and consistency.

This thesis investigates a method of tracking remote transmitters using multipath localization fingerprints generated by a ray tracing simulation. Radio wave propagation phenomena in a dense urban environment can be simulated using publicly available mapping data and 2-D ray tracing techniques when the receivers and transmitters are found to be approximately co-planar. OpenStreetMap (OSM) building data is used to model the area of interest, and frequency of arrival (FOA) localization fingerprints are generated virtually along a grid. These fingerprints are then used to estimate the origin of incoming signals. Simulation of this method using three receivers and a 10 m square grid demonstrates a simulated localization accuracy within 15 m. Taking things a step closer to reality, an attempt to validate the ray tracing simulation is made. Validation is presented in the form of experimental results for a set of trials along with a statistical comparison to simulation results. A significant positive correlation between the experimental and simulation results is found and detailed. Finally, the tracking method described is applied to real collected signal data and the results discussed.

Last decade witnessed the rise of online social networks, especially Twitter. Today, Twitteris a giant social platform with over 250 million users |who produce massive amounts of data everyday. This creates many research opportunities, speci cally for Natural Language Processing (NLP) in which text is utilized to extract information that could be used in many applications. One problem NLP might help solving is geolocation inference or geolocation detection from online social networks. Detecting the location of Twitter users based on the text of their tweets is useful since not many users publicly declare their locations or geotag their tweets. Location information is crucial for a variety of applications such as event detection, disease and illness tracking and user pro ling. These tasks are not trivial, because online content is often noisy; it includes misspellings, incomplete words or phrases, idiomatic expressions, abbreviations, acronyms, and Twitter-speci c literature. In this work, we attempted to detect the location of Canadian users |and tweets sent from Canada |at metropolitan areas and province level; this was not done before, to the best of our knowledge. In order to do this, we collected two di erent datasets, and applied a variety of machine learning, including deep learning methods. Besides, we also attempted to geolocate users based on their social graph (i.e., user's friends and followers) as a novel approach.

This thesis presents and discusses research associated with locating wireless devices. Several algorithms have been developed to determine the physical location of the wireless device and a subset of these algorithms only rely on received signal strength (RSS). Two of the most promising RSS-based algorithms are the LC and dwMDS algorithms; however each algorithm has only been tested via computer simulations with different environmental parameters. To determine which algorithm performs better (i.e., produces estimates that are closer to the true location of the wireless device), a fair comparison needs to be made using the same set of data. The goal of this research is to compare the performance of these two algorithms using not only the same set of data, but data that is collected from the field. An extensive measurement campaign at different environments provided a vast amount of data as input to these algorithms. Both of these algorithms are evaluated in a onedimensional (straight line) and two-dimensional (grid) setting. In total, six environments were used to test these algorithms; three environments for each setting. The results show that on average, the LC algorithm outperforms dwMDS in most of the environments. Since the same data was inputted for each algorithm, a fair comparison can be made and doesn’t give any unfair advantage to any particular algorithm. In addition, since the data was taken directly from the field as opposed to computer simulations, this provides a better degree of confidence for a successful realworld implementation.

Accurate indoor geolocation is an important and novel emerging technology for commercial, public safety, and military applications. Since most wireless communication systems used for indoor position location may suffer from dense multipath situation, which leads to a severe degradation of position accuracy. The improved TDOA/AOA(Time Difference of Arrival/ Angle of Arrival) position location for indoor ultra-wide band (UWB) systems in the thesis improves the position accuracy of indoor location by using fine resolution of UWB signals. In the line of sight situation, by means of increasing angle of arrival (AOA) information to time difference of arrival (TDOA) based location to achieve the goal of accurate indoor geolocation and provides non-line of sight (NLOS) error mitigation for time measurement and AOA selection to suppress the impact to position accuracy in NLOS environment. Finally, the extended Kalman filter is used to perform position tracking of the target. In the simulations, the NLOS error in time measurement is produced according to the characteristics of indoor UWB channel. Several assumptions of NLOS errors are made in angular measurement. It is observed that proposed method efficiently mitigates the position error in NLOS environment, and detect if the NLOS exists between base station and mobile station immediately.

Thesis (Pd. D.)--Worcester Polytechnic Institute. / Keywords: channel measurement; location finding; indoor geolocation; TOA; super-resolution. Includes bibliographical references (p. 165-171).

Dissertation (Ph.D.)--Worcester Polytechnic Institute. / Keywords: Ray Tracing; Wideband Measurement; Dynamic Modeling of Ranging Error; ToA-Based Indoor Localization; NLoS Identification. Includes bibliographical references (leaves 147-159).

Dissertation (Ph.D.)--Worcester Polytechnic Institute. / Keywords: RF system design; indoor positioning. Includes bibliographical references (leaves 270).