We consider time-of-arrival based robust geolocation in harsh line-of-sight/non-line-of-sight environments. Herein, we assume the probability density function (PDF) of the measurement error to be completely unknown and develop an iterative algorithm for robust position estimation. The iterative algorithm alternates between a PDF estimation step, which approximates the exact measurement error PDF (albeit unknown) under the current parameter estimate via adaptive kernel density estimation, and a parameter estimation step, which resolves a position estimate from the approximate log-likelihood function via a quasi-Newton method. Unless the convergence condition is satisfied, the resolved position estimate is then used to refine the PDF estimation in the next iteration. We also present the best achievable geolocation accuracy in terms of the Cramér-Rao lower bound. Various simulations have been conducted in both real-world and simulated scenarios. When the number of received range measurements is large, the new proposed position estimator attains the performance of the maximum likelihood estimator (MLE). When the number of range measurements is small, it deviates from the MLE, but still outperforms several salient robust estimators in terms of geolocation accuracy, which comes at the cost of higher computational complexity.

Positioning is recognized as an important feature of fifth generation (\abbrFiveG) cellular networks due to the massive number of commercial use cases that would benefit from access to position information. Radio based positioning has always been a challenging task in urban canyons where buildings block and reflect the radio signal, causing multipath propagation and non-line-of-sight (NLOS) signal conditions. One approach to handle NLOS is to use data-driven methods such as machine learning algorithms on beam-based data, where a training data set with positioned measurements are used to train a model that transforms measurements to position estimates.  The work is based on position and radio measurement data from a 5G testbed. The transmission point (TP) in the testbed has an antenna that have beams in both horizontal and vertical layers. The measurements are the beam reference signal received power (BRSRP) from the beams and the direction of departure (DOD) from the set of beams with the highest received signal strength (RSS). For modelling of the relation between measurements and positions, two non-linear models has been considered, these are neural network and random forest models. These non-linear models will be referred to as machine learning algorithms.  The machine learning algorithms are able to position the user equipment (UE) in NLOS regions with a horizontal positioning error of less than 10 meters in 80 percent of the test cases. The results also show that it is essential to combine information from beams from the different vertical antenna layers to be able to perform positioning with high accuracy during NLOS conditions. Further, the tests show that the data must be separated into line-of-sight (LOS) and NLOS data before the training of the machine learning algorithms to achieve good positioning performance under both LOS and NLOS conditions. Therefore, a generalized likelihood ratio test (GLRT) to classify data originating from LOS or NLOS conditions, has been developed. The probability of detection of the algorithms is about 90\% when the probability of false alarm is only 5%.  To boost the position accuracy of from the machine learning algorithms, a Kalman filter have been developed with the output from the machine learning algorithms as input. Results show that this can improve the position accuracy in NLOS scenarios significantly. / Radiobasserad positionering av användarenheter är en viktig applikation i femte generationens (5G) radionätverk, som mycket tid och pengar läggs på för att utveckla och förbättra. Ett exempel på tillämpningsområde är positionering av nödsamtal, där ska användarenheten kunna positioneras med en noggrannhet på ett tiotal meter. Radio basserad positionering har alltid varit utmanande i stadsmiljöer där höga hus skymmer och reflekterar signalen mellan användarenheten och basstationen. En ide att positionera i dessa utmanande stadsmiljöer är att använda datadrivna modeller tränade av algoritmer baserat på positionerat testdata – så kallade maskininlärningsalgoritmer. I detta arbete har två icke-linjära modeller - neurala nätverk och random forest – bli implementerade och utvärderade för positionering av användarenheter där signalen från basstationen är skymd.% Dessa modeller refereras som maskininlärningsalgoritmer. Utvärderingen har gjorts på data insamlad av Ericsson från ett 5G-prototypnätverk lokaliserat i Kista, Stockholm. Antennen i den basstation som används har 48 lober vilka ligger i fem olika vertikala lager. Insignal och målvärdena till maskininlärningsalgoritmerna är signals styrkan för varje stråle (BRSRP), respektive givna GPS-positioner för användarenheten. Resultatet visar att med dessa maskininlärningsalgoritmer positioneras användarenheten med en osäkerhet mindre än tio meter i 80 procent av försöksfallen. För att kunna uppnå dessa resultat är viktigt att kunna detektera om signalen mellan användarenheten och basstationen är skymd eller ej. För att göra det har ett statistiskt test blivit implementerat. Detektionssannolikhet för testet är över 90 procent, samtidigt som sannolikhet att få falskt alarm endast är ett fåtal procent.\newline \newline%För att minska osäkerheten i positioneringen har undersökningar gjorts där utsignalen från maskininlärningsalgoritmerna filtreras med ett Kalman-filter. Resultat från dessa undersökningar visar att Kalman-filtret kan förbättra presitionen för positioneringen märkvärt.

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At present, the current 4G systems provide a universal platform for broadband mobile services; however, mobile traffic is still growing at an unprecedented rate and the need for more sophisticated broadband services is pushing the limits on current standards to provide even tighter integration between wireless technologies and higher speeds. This has led to the need for a new generation of mobile communications: the so-called 5G. Although 5G systems are not expected to penetrate the market until 2020, the evolution towards 5G is widely accepted to be the logical convergence of internet services with existing mobile networking standards leading to the commonly used term “mobile internet” over heterogeneous networks, with several Gbits/s data rate and very high connectivity speeds. Therefore, to support highly increasing traffic capacity and high data rates, the next generation mobile network (5G) should extend the range of frequency spectrum for mobile communication that is yet to be identified by the ITU-R. The mm-wave spectrum is the key enabling feature of the next-generation cellular system, for which the propagation channel models need to be predicted to enhance the design guidance and the practicality of the whole design transceiver system. The present work addresses the main concepts of the propagation channel behaviour using ray tracing software package for simulation and then results were tested and compared against practical analysis in a real-time environment. The characteristics of Indoor-Indoor (LOS and NLOS), and indoor-outdoor (NLOS) propagations channels are intensively investigated at four different frequencies; 5.8 GHz, 26GHz, 28GHz and 60GHz for vertical polarized directional, omnidirectional and isotropic antennas patterns. The computed data achieved from the 3-D Shooting and Bouncing Ray (SBR) Wireless Insite based on the effect of frequency dependent electrical properties of building materials. Ray tracing technique has been utilized to predict multipath propagation characteristics in mm-wave bands at different propagation environments. Finally, the received signal power and delay spread were computed for outdoor-outdoor complex propagation channel model at 26 GHz, 28 GHz and 60GHz frequencies and results were compared to the theoretical models.

Nowadays, satellite-based localization is a well-established technical solution to support several navigation tasks in daily life. Besides the application inside of portable devices, satellite-based positioning is used for in-vehicle navigation systems as well. Moreover, due to its global coverage and the availability of inexpensive receiver hardware it is an appealing technology for numerous applications in the area of Intelligent Transportation Systems (ITSs). However, it has to be admitted that most of the aforementioned examples either rely on modest accuracy requirements or are not sensitive to temporary integrity violations. Although technical concepts of Advanced Driver Assistance Systems (ADASs) based on Global Navigation Satellite Systems (GNSSs) have been successfully demonstrated under open sky conditions, practice reveals that such systems suffer from degraded satellite signal quality when put into urban areas. Thus, the main research objective of this thesis is to provide a reliable vehicle positioning concept which can be used in urban areas without the aforementioned limitations. Therefore, an integrated probabilistic approach which preforms fault detection & exclusion, localization and multi-sensor data fusion within one unified Bayesian framework is proposed. From an algorithmic perspective, the presented concept is based on a probabilistic data association technique with explicit handling of outlier measurements as present in urban areas. By that approach, the accuracy, integrity and availability are improved at the same time, that is, a consistent positioning solution is provided. In addition, a comprehensive and in-depth analysis of typical errors in urban areas within the pseudorange domain is performed. Based on this analysis, probabilistic models are proposed and later on used to facilitate the positioning algorithm. Moreover, the presented concept clearly targets towards mass-market applications based on low-cost receivers and hence aims to replace costly sensors by smart algorithms. The benefits of these theoretical contributions are implemented and demonstrated on the example of a real-time vehicle positioning prototype as used inside of the European research project GAlileo Interactive driviNg (GAIN). This work describes all necessary parts of this system including GNSS signal processing, fault detection and multi-sensor data fusion within one processing chain. Finally, the performance and benefits of the proposed concept are examined and validated both with simulated and comprehensive real-world sensor data from numerous test drives.

Nowadays, satellite-based localization is a well-established technical solution to support several navigation tasks in daily life. Besides the application inside of portable devices, satellite-based positioning is used for in-vehicle navigation systems as well. Moreover, due to its global coverage and the availability of inexpensive receiver hardware it is an appealing technology for numerous applications in the area of Intelligent Transportation Systems (ITSs). However, it has to be admitted that most of the aforementioned examples either rely on modest accuracy requirements or are not sensitive to temporary integrity violations. Although technical concepts of Advanced Driver Assistance Systems (ADASs) based on Global Navigation Satellite Systems (GNSSs) have been successfully demonstrated under open sky conditions, practice reveals that such systems suffer from degraded satellite signal quality when put into urban areas. Thus, the main research objective of this thesis is to provide a reliable vehicle positioning concept which can be used in urban areas without the aforementioned limitations. Therefore, an integrated probabilistic approach which preforms fault detection & exclusion, localization and multi-sensor data fusion within one unified Bayesian framework is proposed. From an algorithmic perspective, the presented concept is based on a probabilistic data association technique with explicit handling of outlier measurements as present in urban areas. By that approach, the accuracy, integrity and availability are improved at the same time, that is, a consistent positioning solution is provided. In addition, a comprehensive and in-depth analysis of typical errors in urban areas within the pseudorange domain is performed. Based on this analysis, probabilistic models are proposed and later on used to facilitate the positioning algorithm. Moreover, the presented concept clearly targets towards mass-market applications based on low-cost receivers and hence aims to replace costly sensors by smart algorithms. The benefits of these theoretical contributions are implemented and demonstrated on the example of a real-time vehicle positioning prototype as used inside of the European research project GAlileo Interactive driviNg (GAIN). This work describes all necessary parts of this system including GNSS signal processing, fault detection and multi-sensor data fusion within one processing chain. Finally, the performance and benefits of the proposed concept are examined and validated both with simulated and comprehensive real-world sensor data from numerous test drives.

The purpose of this thesis is to investigate how the LoRa modulation configuration affects precision and accuracy when using LoRa for indoor positioning in non-line-of-sight conditions. The aim is to research if an optimum combination of spreading factor, bandwidth, and code rate factor can be found to result in the best possible positioning accuracy and precision, under certain predefined conditions. An experiment was conducted, where quantitative data was collected from an experimental setup. The experiment consisted of two testbeds which also included an analysis between them. Two kinds of test units were used in the experiment. The LoRa 868 MHz transmitter, which represented the unknown position, and the receivers that were used to estimate the position of the transmitter. The experiment gathered the RSSI values between the transmitter and receivers at different configurations and locations. The data collected from the experiment were analyzed using mathematical theory to answer the research question. The most accurate and precise value for each individual transmission parameter was established in the first testbed and used as the base data rates in the second testbed, to evaluate the best performing parameters simultaneously. The mean accuracy in testbed 1 varied from 180 cm to 388 cm, and the mean precision ranged between 0.432 dBm to 1.298 dBm. The mean accuracy in testbed 2 varied from 341 cm to 455 cm, and the mean precision ranged between 0.275 dBm to 1.495 dBm.  The experimental results indicate no connections between the data rate and precision. No correlation is found between the accuracy and the data rate. The standard deviation and absolute error fluctuate independently of the data rate and the transmitter position. In regard to the given results, the authors conclude that no linear relationship is found between the LoRa modulation configuration and the precision and accuracy of a position, in the selected environment. The experimental results show that LoRa could be used for indoor positioning applications where a rough estimation of a position is adequate since the mean accuracy is quite low for almost all tested modulation configurations. There could be applications where other factors, such as the energy consumption or communication range, are of more importance than accuracy. For those applications, LoRa could still be an adequate choice of technology.