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.

Nowadays, high portion of tactical missiles use gimbaled seeker. For accurate target tracking, the platform where the gimbal is mounted must be stabilized with respect to the motion of the missile body. Line of sight stabilization is critical for fast and precise tracking and alignment. Although, conventional PID framework solves many stabilization problems, it is reported that many PID feedback loops are poorly tuned. In this thesis, recently introduced robot control method, proxy based sliding mode control, is adopted for the line of sight (LOS) stabilization. Before selecting the proposed method, adaptive neural network sliding mode control and fuzzy control are also implemented for comparative purposes. Experimental and simulation results show a satisfactory response of the proxy based sliding mode controller.

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.

Accurate indoor positioning of narrowband Internet-of-Things (IoT) sensors has drawn more attention in recent years. The introduction of Bluetooth Low Energy (BLE) technology is one of the latest developments of IoT and especially applicable for Ultra-Low Power (ULP) applications. BLE is an attractive technology for indoor positioning systems because of its low-cost deployment and reasonable accuracy. Efficient indoor positioning can be achieved by deducing the sensor position from the estimated signal Angle-of-Arrival (AoA) at multiple anchors. An anchor is a base station of known position and equipped with a narrowband multi-antenna array. However, the design and implementation of indoor positioning systems based on AoA measurements involve multiple challenges. The first part of this thesis mainly addresses the impact of hardware impairments on the accuracy of AoA measurements. In practice, the subspace-based algorithms such as Multiple Signal Classification (MUSIC) suffer from sensitivity to array calibration errors coming from hardware imperfections. A detailed experimental implementation is performed using a Software Defined Radio (SDR) platform to precisely evaluate the accuracy of AoA measurements. For this purpose, a new Over-the-Air (OTA) calibration method is proposed and the array calibration error is investigated. The experimental results are compared with the theoretical analysis. These results show that array calibration errors can cause some degrees of uncertainty in AoA estimation. Moreover, we propose iterative positioning algorithms based on AoA measurements for low capacity IoT sensors with high accuracy and fair computational complexity. Efficient positioning accuracy is obtained by iterating between the angle and position estimation steps. We first develop a Data-Aided Maximum a Posteriori (DA- MAP) estimator based on the preamble of the transmitted signal. DA-MAP estimator relies on the knowledge of the transmitted signal which makes it impractical for narrowband communications where the preamble is short. For this reason, a Non-Data- Aided Maximum a Posteriori (NDA-MAP) estimator is developed to improve the AoA accuracy. The iterative positioning algorithms are therefore classified as Data-Aided Iterative (DA-It) and Non-Data-Aided Iterative (NDA-It) depending on the knowledge of the transmitted signal that is used for estimation. Both numerical and experimental analyses are carried out to evaluate the performance of the proposed algorithms. The results show that DA-MAP and NDA-MAP estimators are more accurate than MUSIC. The results also show that DA-It comes very close to the performance of the optimal approach that directly estimates the position based on the observation of the received signal, known as Direct Position Estimation (DPE). Furthermore, the NDA-It algorithm significantly outperforms the DA-It because it can use a much higher number of samples; however, it needs more iterations to converge. In addition, we evaluate the computational savings achieved by the iterative schemes compared to DPE through a detailed complexity analysis. Finally, we investigate the performance degradation of the proposed iterative algorithms due to the impact of multipath and NLOS propagation in indoor environments. Therefore, we develop an enhanced iterative positioning algorithm with an anchor selection method in order to identify and exclude NLOS anchors. The numerical results show that applying the anchor selection strategy significantly improves the positioning accuracy in indoor environments. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

The evolution to ultra-dense next generation networks requires a massive increase in throughput and deployment flexibility. Therefore, novel wireless backhaul solutions that can support these demands are needed. In this work we present an approach for a millimeter wave line-of-sight MIMO backhaul design, targeting transmission rates in the order of 100 Gbit/s. We provide theoretical foundations for the concept showcasing its potential, which are confirmed through channel measurements. Furthermore, we provide insights into the system design with respect to antenna array setup, baseband processing, synchronization, and channel equalization. Implementation in a 60 GHz demonstrator setup proves the feasibility of the system concept for high throughput backhauling in next generation networks.

This thesis deals with testing of astronomical measurement system MAAS-1, which was developed at the Institute of Geodesy, Faculty of Civil Engineering, Brno University of Technology. Several reference measurements were done on the terrace of B building Faculty of Civil Engineering during this testing. The data obtained were processed into astronomical geographic coordinates and . These coordinates, as well as atmospheric conditions and calibration measurements, are basis for evaluation. In this test I try to detect the influence of the CCD sensor mounting of the accuracy on the final data and to assess if the construction needs to be modified.

Indoor positioning is desired in many areas for various reasons, such as positioning products in industrial environments, hospital equipment or firefighters inside a building on fire. One even tougher situation where indoor positioning can be useful is locating a specific object on a shelf in a commercial setting. This thesis aims to investigate and design different network deployment strategies in an indoor environment in order to achieve both high position estimation accuracy and availability. The investigation considers the two positioning techniques downlink time difference of arrival, DL-TDOA, and round trip time, RTT. Simulations of several deployments are performed in two standard scenarios which mimic an indoor open office and an indoor factory, respectively. Factors having an impact on the positioning accuracy and availability are found to be deployment geometry, number of base stations, line-of-sight conditions and interference, with the most important being deployment geometry. Two deployment strategies are designed with the goal of optimising the deployment geometry. In order to achieve both high positioning accuracy and availability in a simple, sparsely cluttered environment, the strategy is to deploy the base stations evenly around the edges of the deployment area. In a more problematic, densely cluttered environment the approach somewhat differs. The proposed strategy is now to identify and strategically place some base stations in the most cluttered areas but still place a majority of the base stations around the edges of the deployment area. A robust positioning algorithm is able to handle interference well and to decrease its impact on the positioning accuracy. The cost, in terms of frequency resources, of using more orthogonal signals may not be worth the small improvement in accuracy and availability.

Fog of War is a term for uncertainty in situational awareness. Fog of War is an essential part of a wargame which causes the participating units’ perception of the environment to be distorted and altered. Introducing a certain amount of uncertainty helps to better mimic the situation on the battlefield. Fog of War comes in multiple forms and levels, whereas the visual detection level is of primary interest for this thesis. Two forms of visual detection have been implemented to simulate a simple and advanced form of Fog of War. The simple level is based solely on the distance between two units, while the advanced level determines whether two units possess a clear line of sight between them, to decisively add realism to a played scenario. The two models were created based on the Entity Component System software architecture, and the maps used for the wargame were based on data from ArcGIS. Extensive testing of the two models, for different types of terrains, show good performance and computational efficiency, however with the expected caveat that flat landscape requires significantly more processing power and memory capacity than a hilly terrain. / Krigsdimma är en term för osäkerhet inom situationsmedvetenhet. Krigsdimma är en väsentlig del av ett krigsspel och medför att deltagande förbands uppfattning av miljön förvrängs och förändras samt att ett visst mått av osäkerhet introduceras för att bättre efterlikna situationen på slagfältet. Krigsdimma kommer i flera former och flera nivåer, där visuell detektering är av primärt intresse för denna avhandling. Två former av visuell detektering har implementerats för att simulera en enkel och en avancerad form av krigsdimma. Den enkla nivån bygger enbart på avståndet mellan två förband medan den avancerade nivån kan avgöra om två enheter i verkligheten har en fri siktlinje mellan sig, något som på ett avgörande sätt kan tillföra realism till ett spelat scenario. De två realiseringarna skapades baserat på en Entity Component System mjukvaruarkitektur, och kartorna som användes för krigsspelet baserade sig på data från ArcGIS. Omfattande tester av de två modellerna, för olika terrängtyper, visar på god funktion och beräkningseffektivitet, dock kräver flackt landskap betydligt mer processorkraft och minneskapacitet än kuperad terräng.

The detection of low-mass extra-solar planets through radial-velocity searches is currently limited by the intrinsic magnetic activity of the host stars. The correlated noise that arises from their natural radial-velocity variability can easily mimic or conceal the orbital signals of super-Earth and Earth-mass extra-solar planets. I developed an intuitive and robust data analysis framework in which the activity-induced variations are modelled with a Gaussian process that has the frequency structure of the photometric variations of the star, thus allowing me to determine precise and reliable planetary masses. I applied this technique to three recently discovered planetary systems: CoRoT-7, Kepler-78 and Kepler-10. I determined the masses of the transiting super-Earth CoRoT-7b and the small Neptune CoRoT-7c to be 4.73 ± 0.95 M⊕ and 13.56 ± 1.08 M⊕, respectively. The density of CoRoT-7b is 6.61 ± 1.72 g.cm⁻³, which is compatible with a rocky composition. I carried out Bayesian model selection to assess the nature of a previously identified signal at 9 days, and found that it is best interpreted as stellar activity. Despite the high levels of activity of its host star, I determined the mass of the Earth-sized planet Kepler-78b to be 1.76 ± 0.18 M⊕. With a density of 6.2(+1.8:-1.4) g.cm⁻³, it is also a rocky planet. I found the masses of Kepler-10b and Kepler-10c to be 3.31 ± 0.32 M⊕ and 16.25 ± 3.66 M⊕, respectively. Their densities, of 6.4(+1.1:-0.7) g.cm⁻³ and 8.1 ± 1.8 g.cm⁻³, imply that they are both of rocky composition – even the 2 Earth-radius planet Kepler-10c! In parallel, I deepened our understanding of the physical origin of stellar radial-velocity variability through the study of the Sun, which is the only star whose surface can be imaged at high resolution. I found that the full-disc magnetic flux is an excellent proxy for activity-induced radial-velocity variations; this result may become key to breaking the activity barrier in coming years. I also found that in the case of CoRoT-7, the suppression of convective blueshift leads to radial-velocity variations with an rms of 1.82 m.s⁻¹, while the modulation induced by the presence of dark spots on the rotating stellar disc has an rms of 0.46 m.s⁻¹. For the Sun, I found these contributions to be 2.22 m.s⁻¹ and 0.14 m.s⁻¹, respectively. These results suggest that for slowly rotating stars, the suppression of convective blueshift is the dominant contributor to the activity-modulated radial-velocity signal, rather than the rotational Doppler shift of the flux blocked by starspots.

La stabilisation inertielle de ligne de visée est essentiellement un problème de rejet de perturbations : il faut rendre la ligne de visée de la caméra embarquée dans le viseur insensible aux mouvements du porteur. Les méthodes de commande robuste du type H-infini sont bien adaptées à la résolution de ce type de problème, et plus particulièrement l’approche Loop-Shaping qui repose sur des concepts de réglage de l’automatique fréquentielle classique. Cependant, les correcteurs obtenus via cette approche sont généralement d’ordre élevé et donc difficilement implémentables sur le calculateur embarqué du viseur.Dans cette thèse, nous avons proposé des méthodologies de synthèse de correcteurs robustes d’ordre réduit et/ou de structure fixée. Pour cela, nos travaux ont été axés sur :- L’optimisation pour la synthèse H-infini à ordre et/ou structure fixée. Tout d’abord nous avons exploré les possibilités offertes par l’optimisation sous contraintes LMI (Linear Matrix Inequalities). Celles-ci se sont avérées limitées, bien que de nombreux algorithmes aient été proposés dans ce cadre depuis le début des années 90. Ensuite, nous avons opté pour l’optimisation non lisse. En effet des outils numériques récemment développés rendent accessible cette approche, et leur efficacité s’est avéré indéniable.- L’adaptation au cadre particulier du critère H-infini Loop-Shaping.La structure particulière de ce critère de synthèse a été exploitée afin de mieux prendre en compte les pondérations, et d’améliorer la réduction d’ordre du correcteur final. Enfin, une approche basée uniquement sur le réglage graphique d’un gabarit de gain fréquentiel en boucle ouverte est proposée. Ces différentes méthodologies sont illustrées, tout au long de la thèse, sur un viseur dont le modèle a été identifié à partir de mesures expérimentales. / Inertial line of sight stabilization is a disturbance rejection problem: the goal is to hold steady in the inertial space, the line of sight of a camera, which is carried on a mobile vehicle. H-infinity robust control techniques are well suited for this type of problem, in particular the Loop-Shaping approach which relies on classical frequency domain concepts. However, this approach results in high order controllers which are hardly implementable on the real time embedded electronic unit of the sight system.In this thesis, fixed order and fixed structure controller design methodologies are proposed. This development follows two main axis: - Fixed order H-infinity Optimization. First, fixed order controllers have been investigated through the LMI (Linear Matrix Inequalities) optimization framework. However the numerical efficiency of this approach is still limited, despite the large amount of research in this area since the 90’s. Then, we used recently developed and more efficient tools that recast the fixed order H-infinity synthesis problem as a nonsmooth optimization problem.- Adaptation to the H-infinity Loop-Shaping frameworkWe adapted the 4 block H-infinity criterion in order to include the weighting filters in the fixed order controller optimization, which enhance the final controller order reduction. Then, we proposed a fixed order controller design approach, based only on graphically tuning a target open loop frequency gain.