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Wireless LANs are becoming more and more popular because they can provide high data rate network access to computer users without the tradition cable. Additionally mobile devices allow people to have connectivity, even when moving from place to place, additionally they are small in size and light in weight. While such devices only offer some of the capabilities of PC they are more convenient to use than a desktop PC when traveling. Cellular phone and smart phones have more function than before, with some newer models phone having an embedded GPS receiver. This GPS receiver can provide user with new services, in addition to location information. Many of these handheld devices include wireless LAN functionality, enabling people to walk from one place to another with a continuous network access, via either the WLAN or the cellular network. By using voice over IP, a wireless enabled hand device becomes a Virtual Cell Phone enabling low cost calls via the WLAN when the user is within coverage of an access point which will give them access, or via the normal cellular phone network (when with in coverage). Because the WLAN coverage is not ubiquitous, it is important to build a Wireless Network Coverage model to enable every user to see where there is WLAN network coverage. Present methods to create such a coverage map require a lot of work to collect data, both indoors and outdoors. Currently a lot of human effort is needed to collect, process, and format this data. The method proposed her could provide an easier way of gathering data from the field and be simple enough that even a normal user could collect data and contribute it to help generate a coverage model of areas where they visit. The measurements reported indicate that it is possible to combine data from multiple devices of the same and different types, but only when the signal strength is high. Fortunately, it is just these areas where the signal quality is good which are of interest to users. The thesis also shows one way of presenting this data in an easy to understand visual manner as an overlay on Google Earth. / Trådlös LANs blir mer och mer populär emedan de kanna skaffa hög datanhastighet nätverken tillträde till computern förbrukaren utantraditionen kabel. Ytterligare rörlig anordningen tillåta folk tillhar connectivity, jämn när flyttanden från ställe till ställe, ochblir liten i storlek och ljus i vikt. Fördriva tiden sådan anordningenbara erbjudande något om anlagen av PC de er mer bekväm till användaän en desktopen PC när resande. Cellular telefonerna och "smart"telefonerna har mer funktion än framför, med något nye modellernatelefonerna har inbyggd GPS. Den här GPS ta emot kanna skaffaförbrukaren med ny tjänsten, dessutom till läge informationen. Mångaav de här handheld omfatta trådlös LAN funktionellitet, sättande istånd till folk till gå från en ställe till en annan med enkontinuerlig nätverken tillträde, via WLAN eller den cellularnätverken. Vid användande röst över IP, en trådlös sättet i stånd tillhand anordning blir en Verklig cellular telefonerna sättande i ståndtill låg kostnad telefonsamtalen via den WLAN när förbrukaren ertäckningenhåll av en tillträde punkt vilken vilja ger dem tillträde,eller via den normal cellular telefonerna nätverken (när i täckningenhåll). Emedan den WLAN täckningen är inte allmänt utbredd, den er viktig tillbygga en Trådlös Nätverken Täckningen modell till möjliggöra varjeförbrukaren till se var där er WLAN nätverken täckningen. Föreställametoderna till skapa sådan en täckningen karta behöva mycket verk tillsamla datan, båda indoors och utomhus. Just nu en masse mänskligansträngning är behövde till samla, förlopp, och formaten den härdatan. Metoden föreslå här kunde skaffa en lättare väg av samlingendatan från fält och bli enkel nog så pass jämn en normal förbrukarenkunde samla datan och bidra med den till hjälp generera en täckningenmodell av områdena var de besöka. Måtten rapportera ange så pass den er möjlig till kombinera datan frånmångfaldig anordningen om det lika och olik typen, utom bara närsignalen styrka är hög. Lyckligtvis, den er rättvis de här områdenavar signalen kvalitet är god vilken är om intresse tillförbrukaren. Theses också visar en väg av presenterande den här datani en lätt till förstå visuell sätt så en täcka över på GoogleEarth.

no / A comparison is presented between two indoor localization algorithms using received signal strength, namely the vector algorithm and the Comparative Received Signal Strength (CRSS) algorithm. Signal values were obtained using ray tracing software and processed with MATLAB to ascertain the effects on localization accuracy of radio map resolution, number of access points and operating frequency. The vector algorithm outperforms the CRSS algorithm, which suffers from ambiguity, although that can be reduced by using more access points and a higher operating frequency. Ambiguity is worsened by the addition of more reference points. The vector algorithm performance is enhanced by adding more access points and reference points while it degrades with increasing frequency provided that the statistical mean of error increased to about 60 cm for most studied cases. / Unable to contact publisher. Contact webform only works for members - no email addresses. Raed said he would try and get contact details - email 14th March 2016 / The full text is unavailable. The publisher is unable to be contacted.

As portable computers and wireless networks are becoming ubiquitous, it is natural to consider the user’s position as yet another aspect to take into account when providing services that are tailored to meet the needs of the consumers. Location aware systems could guide persons through buildings, to a particular bookshelf in a library or assist in a vast variety of other applications that can benefit from knowing the user’s position. In indoor positioning systems, the most commonly used method for determining the location is to collect samples of the strength of the received signal from each base station that is audible at the client’s position and then pass the signal strength data on to a positioning server that has been previously fed with example signal strength data from a set of reference points where the position is known. From this set of reference points, the positioning server can interpolate the client’s current location by comparing the signal strength data it has collected with the signal strength data associated with every reference point. Our work proposes the use of multiple successive received signal strength samples in order to capture periodic signal strength variations that are the result of effects such as multi-path propagation, reflections and other types of radio interference. We believe that, by capturing these variations, it is possible to more easily identify a particular point; this is due to the fact that the signal strength fluctuations should be rather constant at every position, since they are the result of for example reflections on the fixed surfaces of the building’s interior. For the purpose of investigating our assumptions, we conducted measurements at a site at Växjö university, where we collected signal strength samples at known points. With the data collected, we performed two different experiments: one with a neural network and one where the k-nearest-neighbor method was used for position approximation. For each of the methods, we performed the same set of tests with single signal strength samples and with multiple successive signal strength samples, to evaluate their respective performances. We concluded that the k-nearest-neighbor method does not seem to benefit from multiple successive signal strength samples, at least not in our setup, compared to when using single signal strength samples. However, the neural network performed about 17% better when multiple successive signal strength samples were used.

<p>As portable computers and wireless networks are becoming ubiquitous, it is natural to consider the user’s position as yet another aspect to take into account when providing services that are tailored to meet the needs of the consumers. Location aware systems could guide persons through buildings, to a particular bookshelf in a library or assist in a vast variety of other applications that can benefit from knowing the user’s position.</p><p>In indoor positioning systems, the most commonly used method for determining the location is to collect samples of the strength of the received signal from each base station that is audible at the client’s position and then pass the signal strength data on to a positioning server that has been previously fed with example signal strength data from a set of reference points where the position is known. From this set of reference points, the positioning server can interpolate the client’s current location by comparing the signal strength data it has collected with the signal strength data associated with every reference point.</p><p>Our work proposes the use of multiple successive received signal strength samples in order to capture periodic signal strength variations that are the result of effects such as multi-path propagation, reflections and other types of radio interference. We believe that, by capturing these variations, it is possible to more easily identify a particular point; this is due to the fact that the signal strength fluctuations should be rather constant at every position, since they are the result of for example reflections on the fixed surfaces of the building’s interior.</p><p>For the purpose of investigating our assumptions, we conducted measurements at a site at Växjö university, where we collected signal strength samples at known points. With the data collected, we performed two different experiments: one with a neural network and one where the k-nearest-neighbor method was used for position approximation. For each of the methods, we performed the same set of tests with single signal strength samples and with multiple successive signal strength samples, to evaluate their respective performances.</p><p>We concluded that the k-nearest-neighbor method does not seem to benefit from multiple successive signal strength samples, at least not in our setup, compared to when using single signal strength samples. However, the neural network performed about 17% better when multiple successive signal strength samples were used.</p>

RSS-based localization is a promising solution for estimating the position of a non-collaborative emitter using a network of collaborative sensors. This paper examines RSS-based localization and differential RSS (DRSS) localization in the presence of correlated shadowing with no knowledge of the emitter's reference power.  A new non-linear least squares (NLS) DRSS location estimator that uses correlated shadowing information to improve performance is introduced. The existing maximum likelihood (ML) estimator and Cram\' er Rao lower bound (CRLB) for RSS-based localization given do not account for correlated shadowing. This paper presents a new ML estimator and CRLB for RSS-based localization that account for spatially correlated shadowing and imperfect knowledge of the emitter's reference power. The performance of the ML estimator is compared to the CRLB under different simulation conditions. The ML estimator is shown to be biased when the number of sensors is small or the shadowing variance is large. The effects of correlated shadowing on an RSS-based location estimator are thoroughly examined. It is proven that an increase in correlated shadowing will improve the accuracy of an RSS-based location estimator. It is also demonstrated that the ideal sensor geometry which minimizes the average error becomes more compact as correlation is increased. A geometric dilution of precision (GDOP) formulation is derived that provides a metric for the effect of the position of the sensors and emitter on the location estimator performance. A measurement campaign is conducted that characterizes the path loss at 3.4 GHz. The measurements are compared to the log-distance model. The errors between the model and the measurements, which should theoretically be Gaussian, have a Kurtosis value of 1.31. The errors were determined to be spatially correlated with an average correlation coefficient of 0.5 at a distance of 160 meters. The performance of the location estimators in simulation is compared to the performance using measurements from the measurement campaign. The performance is very similar, with the largest difference between the simulated and actual results in the ML estimator. In both cases, the new NLS DRSS estimator outperformed the other estimators and achieved the CRLB. / Master of Science

No / A comparison is presented between two indoor localization algorithms using received signal strength, namely the vector algorithm and the Comparative Received Signal Strength (CRSS) algorithm. Signal values were obtained using ray tracing software and processed with MATLAB to ascertain the effects on localization accuracy of radio map resolution, number of access points and operating frequency. The vector algorithm outperforms the CRSS algorithm, which suffers from ambiguity, although that can be reduced by using more access points and a higher operating frequency. Ambiguity is worsened by the addition of more reference points. The vector algorithm performance is enhanced by adding more access points and reference points while it degrades with increasing frequency provided that the statistical mean of error increased to about 60 cm for most studied cases. / No full text available. Unable to contact the publisher.

Localization accuracy based on RSS (Received Signal Strength) is notoriously inaccurate in the application of wireless sensor networks. RSS is subject to shadowing effects, which is signal attenuation caused by stationary objects in the radio propagation. RSS are actually the result of decay over distances, and random attenuation over different directions. RSS measurement is also affected by antenna orientation. Starting from extracting the statistical orders in the function relationship between RSS and distance, this thesis first shows how non-metric MDS (Multi-Dimensional Scaling) is the suitable method for cooperative localization. Then, several issues are presented and discussed in the application of non-metric MDS, including determining full connections to avoid flip ambiguities, leveraging the proper initial estimation to avert from local minimum solutions, and imposing structural information to bend the localization result to a priori knowledge. To evaluate the solution, data were acquired from different scenarios including accurate radio propagation model, indoor empirical test, and outside empirical test. Experiment results shows that non-metric MDS can only combat the small scale randomness in the shadowing effects. To combat the large scale ones, macro-diversity approaches are further presented including rotating the receiver’s antenna or collecting RSS from more than one mote in the same position. By averaging the measurements from these diversified receivers, simulation results and empirical tests show that shadowing effects can be greatly reduced. Also included in this thesis is how effective packet structures should be designed in the mote programming based on TinyOS to collect different sequences of RSS measurements and fuse them together. / UOIT

Inspired by promotion of commercial applications, support of location-based services to mobile terminals through their current location has been receiving a lot of attention in recent years even though emergency communications is the primary motivation for development of wireless location. A major challenge to wireless location technique is how to balance the implementation complexity and required accuracy. In the first part of this dissertation, we address one of the fundamental problems in wireless location when using the ToA measurements and develop a simple model to estimate the mobile terminal location with low complexity and promising accuracy. The model employs the geometrical transformation method with single propagation delay measurement. The contribution is that the use of geometrical transformation allows us to overcome the location handover problem, i.e., a forcing handover in a GSM (global system for mobile) network or a three-way soft handover in a UMTS (universal mobile telecommunications system) network. By using the proposed location model, the impact on network performance is kept at the minimum level and the complexity and requirements for hardware and software changes are reduced. In the second part of this dissertation, we address one of the fundamental problems in wireless location when using the SS (signal strength) measurements. The first contribution is to develop a novel wireless location technique based on a ¡§differ- encing¡¨ way, called the SSSD (stationary signal-strength-difference), to remove the uncertainty propagation parameters when merging environment-dependent signal propagation model into the location estimation. This is due to the uncertainty in propagation parameters causes a propagation model error that enlarges error in the distance estimation. The performance gained from the preliminary analysis of SSSD location technique, however, is degraded as a result of the large bias error in the estimated distance and distance difference. To achieve the performance enhancement, the second contribution is to correct the bias error in the estimated distance difference by using a correction method based on a geometric constraint condition. With the corrected distance difference, the final contribution is that we generalized the work on correction method and provide a new framework to correct the error in the estimated distance. As the corrected distance and distance difference is derived by LS (least square) computation, respectively, low computation burden and non-iterative solutions were achieved. To the best of our knowledge thus far, this is first such proposal for a correction to the SS-based location technique. It is demonstrated that the proposed error correction method is shown to perform well when encountering the large error in the estimated distance and distance difference, and prove that the location accuracy can be improved considerably.

M.Ing. / The research and development contained herein details a Proof of Concept to test the commercial viability of a mobile cellular positioning technique for use in Location Based Service applications. Research in this field is relatively new and although the theories of location estimation have been considered at both an industry and academic level respectively, they are still largely isolated. We propose to bridge the gap with a practical implementation of a suitable location based service theory. This implemented theory is a trade-off between cost, performance, accuracy and ease of implementation. The research component of this dissertation constitutes a detailed background and technical overview of second generation mobile cellular technology, investigation of various radio path loss model theories, understanding of the various Location Positioning technologies and exploration into optimised trilateration techniques. The development is amalgamated in an algorithm incorporating radio signal strength analysis, mobile network parameter and management reporting, empirical mobile radio path loss prediction models and adaptive geometric, trigonometric and algebraic trilateration techniques. Furthermore, we conduct vigorous testing of the implemented algorithm with live mobile network radio parameters recorded across various geographical locations which vary in topography. The testing phases include performance gain comparisons between the trilateration techniques. Finally, we show that the selected location estimation theory can be enhanced to be an attractive practical implementation which satisfies the trade-off variables, required for commercial application. The selected theory produces improved location estimate accuracy with cost efficient ease of implementation from among the other theories, when combined with the applied trilateration techniques. The end result is an alternate way to successfully emulate GPS positioning – which is considered to be the global benchmark