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.

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

"Localization inside the human body using Radio Frequency (RF) transmission is gaining importance in a number of applications such as Wireless Capsule Endoscopy. The accuracy of RF localization depends on the technology adopted for this purpose. The two most common RF localization technologies use Received Signal Strength (RSS) and Time-Of-Arrival (TOA). This research first provides bounds for accuracy of localization of a Endoscopy capsule inside the human body as it moves through the gastro-Intestinal track with and without randomness in transmit power using RSS based localization with a triangulation algorithm. It is observed that in spite of presence of a large number of anchor nodes; the localization error is still in range of few cm, which is quite high; hence we resort to TOA based localization. Due to lack of a widely accepted model for TOA based localization inside human body we use a computational technique for simulation inside and around the human body, named Finite Difference Time Domain (FDTD). We first show that our proprietary FDTD simulation software shows acceptable results when compared with real empirical measurements using a vector network analyzer. We then show that, the FDTD method, which has been used extensively in all kinds of electromagnetic modeling due to its versatility and simplicity, suffers seriously because of its demanding requirement on memory storage and computation time, which is due to its inherently recursive nature and the need for absorbing boundary conditions. In this research we suggest a novel computationally efficient technique for simulation using FDTD by considering FDTD as a Linear Time Invariant (LTI) system. Then we use the software to simulate the TOA of the narrowband and wideband signals propagated inside the human body for RF localization to compare the accuracies of the two using this method. "

This thesis analyzes the wireless propagation modeling of a 2.6 GHz band channel around the Tampa Bay area. Different empirical models are compared against measured data, and an adapted model, specific for the Tampa Bay area, is presented that builds on the accuracy of existing models. The effects of the propagation characteristics along bridges are also discussed, and a two-slope model is presented. The proposed models are based on a simple linear regression method, and statistical tests are evaluated for reliability thereof. The analysis also investigates the statistical properties of shadowing effects imposed on the wireless channel. The spatial correlation properties of shadowing effects are investigated in detail, and an extension of existing correlation models for shadowing effects is suggested where the correlation properties are studied in different distance ranges rather than the whole service coverage area.

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>

The main objective of this thesis is to propose a UAV (also called as drones) location estimation system based on LoRaWAN using received signal strength indicator in a GPS denied environment. The drones are finding new applications in areas such as surveillance, search, rescue missions, package delivery, and precision agriculture. Nearly all applications require the localization of UAV during flight. Localization is the method of determining a UAVs physical position using a real or virtual coordinate system. This thesis proposes a LoRaWAN-based UAV location method and presents experimental findings from a prototype. The thesis mainly consists of two different sections: one is the distance estimation and the other is the location estimation. First, the distance is estimated based on the mean RSSI values which are recorded at the ground stations using the path loss model. Later using the slant distance estimation technique, the path loss model parameters L and C are estimated whose values are unknown at the beginning. These values completely depend on the environment. Finally, the trilateration system architecture is employed to find the 3-D location of the UAV.

O crescimento na utilização de redes de sensores sem fio possibilitou desenvolver melhorias para atender as necessidades da indústria de comunicação de dispositivos em função de diversas vantagens relacionadas a baixo custo, baixo consumo de energia, mobilidade, instalação e configuração de novos dispositivos, além disso, criar funcionalidades adicionais, como por exemplo, localização de engenheiros de campo e ativos em ambientes industriais. O protocolo WirelessHART é um padrão aberto de comunicação sem fio que busca atender a estes requisitos. Neste trabalho é apresentado o estudo e desenvolvimento de uma aplicação de localização de um objeto alvo através da rede WirelessHART. São analisados diferentes métodos para estimar a distância entre o objeto alvo e os outros elementos fixos da rede de sensores sem fio, algoritmos para computar dados e determinar a localização em um plano de coordenadas. O funcionamento do sistema proposto e o método de localização utilizado foram avaliados por meio de simulações e de testes práticos. Para as condições utilizadas de instalação dos dispositivos, foi possível obter um alcance na comunicação via rádio de mais de 100 m, o que permitiu determinar uma área de monitoração do sistema de cerca de 100 m x 100 m. Os resultados obtidos do erro de localização atingiram entre um 72% e 80% de estimativas de localização menores que a 5 metros. O trabalho abordou a criação e avaliação de critérios de escolha para obter conjuntos de três transmissores com probabilidade de erros menores do que 5 metros. A avaliação dos critérios é feita através da construção de uma tabela de probabilidade conjunta obtida a partir da aplicação da regra de Bayes em dados experimentais com erro de posicionamento conhecido. / The growth in the use of wireless sensor networks has made possible the development of improvements that meet the needs of the communication industry, including several devices with to low cost, low power consumption, mobility and ease of integration, installation and configuration advantages. Not only that but they also create additional functionalities, such as showing the location of field engineers who are assets in industrial environments. The WirelessHART protocol is an open standard for wireless communication that seeks to meet these qualities. This study presents a development of an application for the localization of a mobile device via the WirelessHART network. Different methods are analyzed to estimate the distance between the mobile node and the other fixed elements of the wireless sensor network, such as deployment topologies and algorithms, which are used to compute the data and determine the location in a coordinate plane. The operation of the proposed system and the location method were evaluated by means of simulations and practical tests. Due to the conditions used for the installation of the devices, it was possible to obtain a range of radio transmission over 100 m, which allowed to determine an area of monitoring system of about 100 m x 100 m. The results obtained from the error location reached between a 72% and 80% of the estimated localization that was less than 5 meters. The study addressed the establishment and evaluation of selection criteria for sets of three transmitters with probability of less than 5 meters errors. The evaluation criteria is done by constructing a joint probability table obtained from the application of Bayes rule in experimental data with known positioning error.