Sensor network performance is governed by the physical placement of sensors and their geometric relationship to the events they measure. To illustrate this, the entirety of this thesis covers the following interconnected subjects: 1) graphical analysis of the variance of the estimation error caused by physical characteristics of an acoustic target source and its geometric location relative to sensor arrays, 2) event/anomaly detection method for time aggregated point sensor data using a parametric Poisson distribution data model, 3) a sensor reduction or placement technique using Bellman optimal estimates of target agent dynamics and probabilistic training data (Goode, Chin, & Roan, 2011), and 4) transforming event monitoring point sensor data into event detection and classification of the direction of travel using a contextual, joint probability, causal relationship, sliding window, and geospatial intelligence (GEOINT) method. / Master of Science

In the sixth generation of wireless communication systems (6G), there exist multiple candidate enabling technologies that help the wireless network satisfy the ever-increasing demand for speed, coverage, reliability, and mobility. Among these technologies, reconfigurable intelligent surfaces (RISs) extend the coverage of a wireless network into dead zones, increase capacity, and facilitate integrated sensing and communications tasks by consuming very low power, thus contributing to energy efficiency as well. RISs are meta-material-based devices whose electromagnetic reflection characteristics can be controlled externally to cater to the needs of the communication links. Most ubiquitously, this comes in the form of adding a desired phase shift to an incident wave before reflecting it, which can be used to phase-align multiple incident waves to increase the strength of the signal at the receiver and provide coverage to an area that otherwise would be a dead zone. While this portrays an image of a dream technology that would boost the existing wireless networks significantly, RISs do not come without engineering problems. First of all, the individual elements do not exhibit ideal reflection characteristics, that is, they attenuate the incident signal in a fashion depending on the configured phase shift. This creates the phenomenon called "phase-dependent amplitude". Another problem caused by RISs is the channel estimation overhead. In a multiple-antenna communication system, the channel between two terminals is as complex as the product of the number of antennas at each end. However, when an RIS comes into the equation, the cascade of the transmitter-RIS and RIS-receiver channels has a complexity further multiplied by the number of RIS elements. Consequently, the channel estimation process to utilize the RIS effectively becomes more demanding, that is, more pilot signals are required to estimate the channel for coherent reception. This adversely affects the effective data rate within a communication system since more resources need to be spent for pilot transmission and fewer resources can be allocated for data transmission. While there exists some work on reducing the channel dimensions by exploiting the channel structure, this problem persists for unstructured channels. In addition, for the wireless networks using multiple RISs, a new kind of pilot contamination arises, which is the main topic of this thesis. In the first part of this thesis, we study this new kind of pilot contamination in a multi-operator context, where two operators provide services to their respective served users and share a single site. Each operator has a single dedicated RIS and they use disjoint frequency bands, but each RIS inadvertently reflects the transmitted uplink signals of the user equipment devices in multiple bands. Consequently, the concurrent reflection of pilot signals during the channel estimation phase introduces a new inter-operator pilot contamination effect. We investigate the implications of this effect in systems with either deterministic or correlated Rayleigh fading channels, specifically focusing on its impact on channel estimation quality, signal equalization, and channel capacity. The numerical results demonstrate the substantial degradation in system performance caused by this phenomenon and highlight the pressing need to address inter-operator pilot contamination in multi-operator RIS deployments. To combat the negative effect of this new type of pilot contamination, we propose to use orthogonal RIS configurations during uplink pilot transmission, which can mitigate or eliminate the negative effect of inter-operator pilot contamination at the expense of some inter-operator information exchange and orchestration. In the second part of this thesis, we consider a single-operator-two-RIS integrated sensing and communication (ISAC) system where the single user is both a communication terminal and a positioning target. Based on the uplink positioning pilots, the base station aims to estimate both the communication channel and the user's position within the indoor environment by estimating the angle of arrival (AoA) of the impinging signals on both RISs and then exploiting the system and array geometries to estimate the user position and user channels respectively. Although there is a single operator, due to the presence of multiple RISs, pilot contamination occurs through the same physical means as multi-operator pilot contamination unless the channel estimation process is parameterized. Since the communication links are considered to be pure line-of-sight (LOS), their structure allows the reduction of the number of unknown parameters. Consequently, the reduction of information caused by pilot contamination does not affect the channel estimation procedure, hence the pilot contamination is overcome. On the other hand, the position of the user is determined by intersecting the lines drawn along the AoA estimates. We adopt the Cramér-Rao Lower Bound (CRLB), the lower bound on the mean squared error (MSE) of any unbiased estimator, for both channel estimation and positioning. Our numerical results show that it is possible to utilize positioning pilots for parametric channel estimation when the wireless links are LOS. / <p>QC 20240416</p>

The rising interest in autonomous systems has emphasized the significance of effective path and motion planning, particularly in coordinating multiple Unmanned Areal Vehicles (UAVs) in missions. An important research field is the problem of Multi-Agent Path Finding (MAPF), in which the objective is to find collision-free paths for multiple agents simultaneously. Various algorithms, categorized into optimal, bounded sub-optimal, and unbounded sub-optimal solvers, have been investigated in order to address MAPF problems. However, recent attention has shifted towards MAPF with kinematic constraints, particularly focusing on nonholonomic agents like cars and fixed-wing UAVs. These nonholonomic agents, distinguished by their motion constraints, require specialized methods for trajectory planning.  To investigate the potential of MAPF with nonholonomic agents, two MAPF algorithms have been implemented, incorporating the kinematic constraints of a fixed-wing UAV. The first algorithm is a UAV-like Conflict-Based Search (CBS) algorithm, belonging to the optimal MAPF solver class, and is based on a Car-like CBS algorithm. The second algorithm is a Prioritized Planner, belonging to the search-based MAPF solver class. Both algorithms utilize a common single-agent search algorithm, the Spatiotemporal Hybrid A* (SHA*), which has been enhanced to incorporate a kinematic bicycle model. This enhancement allows for a greater variety of motions, creates feasible paths for fixed-wing UAVs, and enables control over acceleration and steering rates. A comparison of the two MAPF algorithms was conducted for three different map instances. Furthermore, the use of weighted heuristics, resampling and distance-based priority have been implemented and simulated with the Prioritized Planner. Additionally, two methods of simultaneous arrival have been implemented using the UAV-like CBS, where agents have a fixed time of arrival and a variable time of arrival. The results from the simulations confirm the trade-offs between both MAPF algorithms concerning solution quality, success rate and runtime. The UAV-like CBS is capable of finding solutions of higher quality, while the Prioritized Planner is faster at finding solutions and more efficient for an increasing number of agents. However, the performance of the two algorithms varied significantly, depending on the scenario. The thesis concludes that both algorithms can be utilized for MAPF with nonholonomic fixed-wing UAVs, and that the UAV-like CBS is the best choice for a lower amount of agents, while the Prioritized Planner is preferable for a higher amount of agents. The priority of the agents has been shown to be important, and by allowing resampling, the success rate of the Prioritized Planner can be increased significantly. Additionally, simultaneous arrival at the goal position can be achieved optimally for the UAV-like CBS by solving the problem backwards.

No description available.

In modern combat aircraft, the most common localization method integrates a Global Navigation Satellite System (GNSS) with an Inertial Navigation System (INS). Although GNSS is the optimal choice for navigation, there are situations when the GNSS satellite signal is unavailable. This can happen due to various reasons such as jamming, physical obstacles, or technical failures. An alternative method to GNSS is utilizing Signals of Opportunity (SOP), which leverages signals not intended for navigation, such as those from cellular towers. These signals are transmitted from non-controllable sources, and challenges may arise due to the lack of guarantee regarding their quality and availability. Therefore, it is crucial that any estimation method utilizing SOP is robust to ensure accurate aircraft localization. This thesis investigates three different localization approaches to address this challenge. This study explores SOP sources with both known and unknown positions. For known signal source positions, an Extended Kalman Filter (EKF) based solution is utilized as a baseline to evaluate how well unknown signal sources can be used to estimate the aircraft's location. To address the challenge of unknown signal source positions, an EKF combined with a Simultaneous Localization and Mapping (SLAM) method, referred to as EKF SLAM, is used. In this case, the sources are introduced through two different approaches. The first approach, undelayed initialization, introduces the signal source directly when observed. The second approach, delayed initialization, involves inverse depth parameterization (IDP) and preprocessing of the signal source position before fully introducing it into the aircraft system. While both approaches outperform an unassisted INS approach, they do not achieve the same level of performance as when the source positions are known. Moreover, various factors, including the aircraft's trajectory, measurement noise, measurement frequency, and the initial covariance of new landmarks, influence the performance of the EKF SLAM approaches. Additionally, delayed initialization is strongly influenced by a threshold assessing landmark position estimate linearity, underscoring its sensitivity to accuracy. The concept behind delayed initialization aims to reduce the error of the signal source position before it is introduced to the system. This method has been proven to significantly reduce the signal source position error. However, its robustness is influenced by several factors, including the parallax angle, sudden changes in the aircraft's direction, and particularly the initial covariance of a landmark estimate. The accuracy of the aircraft's position is crucial, resulting in a trade-off between preprocessing and rapidly initializing a signal source position to the aircraft system. In contrast, undelayed initialization is less sensitive to trajectory changes, even though it introduces the signal sources with greater initial error. There is a significant difference in computational time when comparing known and unknown sources. As the number of sources increases, the computational time for unknown sources is more affected than for known sources. The delayed source initialization method increases computational time due to its preprocessing, especially as more sources are used. Conversely, initializing sources directly reduces the computational time, as no preprocessing is required. / I moderna stridsflygplan är den vanligaste lokaliseringsmetoden att integrera ett Global Navigation Satellite System (GNSS) med ett Inertial Navigation System (INS). Även om GNSS är det optimala valet för navigation finns det situationer när GNSS-satellitsignalen inte är tillgänglig. Detta kan inträffa på grund av olika orsaker som störningar, fysiska hinder eller tekniska fel. En alternativ metod till GNSS är att använda Signals of Opportunity (SOP), som utnyttjar signaler som inte är avsedda för navigation, till exempel de från mobilmaster. Dessa signaler kommer från okontrollerbara källor, vilket kan medföra utmaningar på grund av att deras kvalitet och tillgänglighet inte kan garanteras. Därför är det viktigt att varje lokaliseringsmetod som använder SOP är robust för att säkerställa en bra och korrekt flygplans positionering. Detta examensarbete undersöker tre olika lokaliseringsmetoder för att hantera denna utmaning. Denna studie utforskar SOP-källor med både kända och okända positioner. För kända positioner används en lösning baserad på ett Extended Kalman Filter (EKF) som en baslinje för att utvärdera hur väl okända signalkällor kan användas för att uppskatta flygplanets position. För att hantera utmaningen med okända signalkällors positioner används ett EKF kombinerad med en metod vid namn Simultaneous Localization and Mapping (SLAM), även kallad EKF SLAM. I detta fall introduceras källorna genom två olika tillvägagångssätt. Det första tillvägagångssättet, ofördröjd initialisering, introducerar signalkällan direkt när den observeras. Det andra tillvägagångssättet, fördröjd initialisering, involverar inverse depth parameterization (IDP) och förbearbetning av signalkällans position innan den introduceras i flygplanets lokaliseringssystem. Även om båda tillvägagångssätten presterar bättre än en oassisterad INS-metod uppnår de inte samma prestandanivå som när källornas position är kända. Dessutom påverkar olika faktorer prestandan hos EKF SLAM-metoderna, vilka främst är flygplanets flygbana, mätbrus, mätfrekvens och den initiala kovariansen av nya landmärken. Dessutom påverkas fördröjd initialisering starkt av en tröskel som bedömer linjäritet hos landmärkes positionen, vilket understryker dess känslighet för noggrannhet. Konceptet bakom fördröjd initialisering syftar till att minska felet i signalkällans position innan den introduceras i lokaliseringssystemet. Denna metod har visat sig kunna minska felet i signalkällans position avsevärt. Emellertid påverkas dess robusthet av flera faktorer, inklusive parallaxvinkeln, plötsliga förändringar i flygplanets riktning och särskilt den initiala kovariansen av uppskattningen av ett landmärkes position. Noggrannheten i flygplanets position är avgörande, vilket resulterar i en avvägning mellan förbearbetning och snabb initialisering av en signalkällas position till flygplanets lokaliseringssystem. Till skillnad från fördröjd initialisering är ofördröjd initialisering mindre känslig för förändringar i flygbanan, även om den introducerar signalkällorna med större initialt fel. Det finns en anmärkningsvärd skillnad i beräkningstid när man jämför kända och okända källors. När antalet källor ökar påverkas beräkningstiden för okända källor mer än för kända källor. Den fördröjda källinitialiseringsmetoden ökar beräkningstiden på grund av dess förbearbetning, särskilt när många källor används. Däremot minskar beräkningstiden när källor initialiseras direkt, eftersom ingen förbearbetning krävs.

Vision has always been one of the most important cognitive tools of human beings. In this regard, the development of image sensors opens up the potential to view objects that our eyes cannot see. One of the most promising capability in some image sensors is their single-photon sensitivity that provides information at the ultimate fundamental limit of light. Time-resolved single-photon avalanche diode (SPAD) image sensors bring a new dimension as they measure the arrival time of incident photons with a precision in the order of hundred picoseconds. In addition to this characteristic, they can be fabricated in complementary metal-oxide-semiconductor (CMOS) technology enabling the integration of complex signal processing blocks at the pixel level. These unique features made CMOS SPAD sensors a prime candidate for a broad spectrum of applications. This thesis is dedicated to the optimization and characterization of quantum imagers based on the SPADs as part of the E.U. funded SUPERTWIN project to surpass the fundamental diffraction limit known as the Rayleigh limit by exploiting the spatio-temporal correlation of entangled photons. The first characterized sensor is a 32×32-pixel SPAD array, named “SuperEllen”, with in-pixel time-to-digital converters (TDC) that measure the spatial cross-correlation functions of a flux of entangled photons. Each pixel features 19.48% fill-factor (FF) in 44.64-μm pitch fabricated in a 150-nm CMOS standard technology. The sensor is fully characterized in several electro-optical experiments, in order to be used in quantum imaging measurements. Moreover, the chip is calibrated in terms of coincidence detection achieving the minimal coincidence window determined by the SPAD jitter. The second developed sensor in the context of SUPERTWIN project is a 224×272-pixel SPAD-based array called “SuperAlice”, a multi-functional image sensor fabricated in a 110-nm CMOS image sensor technology. SuperAlice can operate in multiple modes (time-resolving or photon counting or binary imaging mode). Thanks to the digital intrinsic nature of SPAD imagers, they have an inherent capability to achieve a high frame rate. However, running at high frame rate means high I/O power consumption and thus inefficient handling of the generated data, as SPAD arrays are employed for low light applications in which data are very sparse over time and space. Here, we present three zero-suppression mechanisms to increase the frame rate without adversely affecting power consumption. A row-skipping mechanism that is implemented in both SuperEllen and SuperAlice detects the absence of SPAD activity in a row to increase the duty cycle. A current-based mechanism implemented in SuperEllen ignores reading out a full frame when the number of triggered pixels is less than a user-defined value. A different zero-suppression technique is developed in the SuperAlice chip that is based on jumping through the non-zero pixels within one row. The acquisition of TDC-based SPAD imagers can be speeded up further by storing and processing events inside the chip without the need to read out all data. An on-chip histogramming architecture based on analog counters is developed in a 150-nm CMOS standard technology. The test structure is a 16-bin histogram with 9 bit depth for each bin. SPAD technology demonstrates its capability in other applications such as automotive that demands high dynamic range (HDR) imaging. We proposed two methods based on processing photon arrival times to create HDR images. The proposed methods are validated experimentally with SuperEllen obtaining &gt;130 dB dynamic range within 30 ms of integration time and can be further extended by using a timestamping mechanism with a higher resolution.

[pt] Esta tese desenvolve técnicas avançadas de processamento de sinais com arranjos de sensores, tanto para arranjos completamente calibrados quanto parcialmente calibrados. São propostas novas geometrias de arranjos esparsos baseadas em subarranjos lineares esparsos, bem como são desenvolvidos novos algoritmos de estimativa de direção de chegada (DOA) para sinais eletromagnéticos de banda estreita, utilizando-se a teoria de processamento estatístico. Os algoritmos propostos, denominados Generalized Coarray MUSIC (GCA-MUSIC) e Generalized Coarray Root MUSIC (GCA-rMUSIC), expandem a técnica clássica denominada Multiple Signal Classification (MUSIC) para configurações de subarranjos esparsos. Técnicas de projeto de subarranjos lineares esparsos foram propostas, assim como uma análise dos graus de liberdade dos subarranjos (sDoF) em função dos graus de liberdade do arranjo completo (DoF). Além disso, desenvolvem-se versões com tamanho de Janela Variável (VWS) desses algoritmos, que incorporam técnicas de suavização espacial com abertura variável. Esses métodos proporcionam estimativas de direção de alta precisão e conseguem estimar um número maior de fontes do que o número de sensores físicos em cada subarranjo, explorando estruturas de coarranjo específicas. A análise de desempenho demonstra que o GCA-MUSIC e o GCA-rMUSIC, juntamente com suas variantes VWS, melhoram a precisão no contexto de arranjos parcialmente calibrados, onde podem existir incertezas de calibração. Além disso, são apresentadas variantes VWS do algoritmo Coarray MUSIC (CA-MUSIC) para arranjos totalmente calibrados (coerentes), permitindo estratégias de suavização adaptáveis para um desempenho aprimorado. Além do desenvolvimento algorítmico, foram derivadas as Matrizes de Informação de Fisher (FIMs) para o conjunto completo de parâmetros deste modelo de dados generalizado, incluindo tanto as relações de parâmetros consigo próprios quanto cruzados. Essas matrizes levam em consideração as direções das fontes, potências das fontes, potência do ruído e as componentes reais e imaginárias de todos os parâmetros de calibração, representando cenários com fontes correlacionadas e descorrelacionadas. Este trabalho avança significativamente a compreensão teórica dos limites de desempenho da estimativa de direções, fornecendo uma quantificação mais rigorosa dos limitantes de Cramér-Rao. Esses limitantes são particularmente relevantes em cenários com arranjos parcialmente calibrados e fontes descorrelacionadas, conforme demonstrado utilizando-se modelos de dados baseados no produto de Khatri-Rao. / [en] This thesis explores advanced array signal processing techniques for both fully and partially calibrated arrays. We introduce novel sparse array geometries based on sparse linear subarrays and develop new direction-of-arrival (DOA) estimation algorithms for narrowband electromagnetic signals, framed within statistical signal processing principles. The proposed algorithms, named Generalized Coarray MUSIC (GCA-MUSIC) and Generalized Coarray Root MUSIC (GCA-rMUSIC), extend the classical Multiple Signal Classification (MUSIC) framework to sparse subarrays configurations. Sparse linear subarray design techniques were proposed, as well as an analysis of the degrees of freedom of subarrays (sDoF) as a function of degrees of freedom of the whole array (DoF). Additionally, we develop Variable Window Size (VWS) versions of these algorithms, which incorporate flexible spatial smoothing apertures. These methods provide high-accuracy DoA estimates and offer the key advantage of resolving more sources than the number of physical sensors in each subarray by exploiting coarray structures. Performance analysis demonstrates that GCA-MUSIC and GCA-rMUSIC, along with its VWS variants, improve accuracy in the context of partially-calibrated arrays, where calibration uncertainties may exist. Furthermore, VWS variants of the Coarray MUSIC (CA-MUSIC) algorithm are presented for fully calibrated (coherent) arrays, enabling adaptable smoothing strategies for enhanced performance. In addition to algorithmic development, we compute the Fisher Information Matrices (FIMs) for the complete set of parameters in this generalized data model, including both self and cross-coupled parameter relationships. These matrices account for source directions, source powers, noise power, and the real and imaginary components of all calibration parameters, representing both correlated and uncorrelated source scenarios. This work significantly advances the theoretical understanding of DoA estimation performance limits by providing a more rigorous quantification of the Cramér-Rao bounds. These bounds are particularly relevant in scenarios with partially calibrated arrays and uncorrelated sources, as demonstrated using the Khatri-Rao product-based data model.

Les premières recherches en détection, localisation et séparation de signaux remontent au début du 20ème siècle. Ces recherches sont d’actualité encore aujourd’hui, notamment du fait de la croissance rapide des systèmes de communications constatée ces deux dernières décennies. Par ailleurs, la littérature du domaine consacre très peu d’études relatives à certains contextes jugés difficiles dont certains sont traités dans cette thèse. Ce travail porte sur la localisation de signaux par détection des temps d’arrivée ou estimation des directions d’arrivée et sur la séparation de sources dépendantes ou à module constant. L’idée principale est de tirer profit de certaines informations a priori disponibles sur les signaux sources telles que la parcimonie, la cyclostationarité, la non-circularité, le module constant, la structure autoregressive et les séquences pilote dans un contexte coopératif. Une première partie détaille trois contributions : (i) un nouveau détecteur pour l’estimation des temps d’arrivée basé sur la minimisation de la probabilité d’erreur ; (ii) une estimation améliorée de la puissance du bruit, basée sur les statistiques d’ordre ; (iii) une quantification de la précision et de la résolution de l’estimation des directions d’arrivée au regard de certains a priori considérés sur les sources. Une deuxième partie est consacrée à la séparation de sources exploitant différentes informations sur celles-ci : (i) la séparation de signaux de communication à module constant ; (ii) la séparation de sources dépendantes connaissant la nature de la dépendance et (iii) la séparation de sources autorégressives dépendantes connaissant la structure autorégressive. / Signal detection, localization, and separation problems date back to the beginning of the twentieth century. Nowadays, this subject is still a hot topic receiving more and more attention, notably with the rapid growth of wireless communication systems that arose in the last two decades and it turns out that many challenging aspects remain poorly addressed by the available literature relative to this subject. This thesis deals with signal detection, localization using temporal or directional measurements, and separation of dependent source signals. The main objective is to make use of some available priors about the source signals such as sparsity, cyclo-stationarity, non-circularity, constant modulus, autoregressive structure or training sequences in a cooperative framework. The first part is devoted to the analysis of (i) signal’s time-of-arrival estimation using a new minimum error rate based detector, (ii) noise power estimation using an improved order-statistics estimator and (iii) side information impact on direction-of-arrival estimation accuracy and resolution. In the second part, the source separation problem is investigated at the light of different priors about the original sources. Three kinds of prior have been considered : (i) separation of constant modulus communication signals, (ii) separation of dependent source signals knowing their dependency structure and (iii) separation of dependent autoregressive sources knowing their autoregressive structure.

Diplomová práce shrnuje nejdůležitější informace o letectví, jako například základní používané termíny, popis letových fází apod. V této práci je popsán flight management system, jeho funkce a schopnosti vytvořit cenově příznivý a současně absolutně spolehlivý letový plán. V další části práce je nastíněna důležitost předpovědi počasí pro bezpečnou a současně cenově příznivou leteckou dopravu. Tato práce je vytvořena v programu Matlab a všechny bloky jsou naprogramovány jako m-funkce. Důležité části kódu jsou z důvodu názornosti zobrazeny jako vývojové diagramy. Praktická část práce je rozdělena do několika podkapitol, kde každá podkapitola popisuje jeden blok z blokového schématu pro výpočet nejistoty odhadované doby příletu. Současně je zde vysvětlena funkce ostatních bloků pro plánování letu, předpověď počasí, kombinování větrů a výpočet odhadnuté doby příletu a její nejistoty.

Mobile phone tracking is the ascertaining of the position or location of a mobile phone when moving from one place to another place. Location Based Services Solutions include Mobile positioning system that can be used for a wide array of consumer-demand services like search, mapping, navigation, road transport traffic management and emergency-call positioning. The Mobile Positioning System (MPS) supports complementary positioning methods for 2G, 3G and 4G/LTE (Long Term Evolution) networks. Mobile phone is popularly known as an UE (User Equipment) in LTE. A prototype method of live trajectory estimation for massive UE in LTE network has been proposed in this thesis work. RSRP (Reference Signal Received Power) values and TA(Timing Advance) values are part of LTE events for UE. These specific LTE events can be streamed to a system from eNodeB of LTE in real time by activating measurements on UEs in the network. AoA (Angle of Arrival) and TA values are used to estimate the UE position. AoA calculation is performed using RSRP values. The calculated UE positions are filtered using Particle Filter(PF) to estimate trajectory. To obtain live trajectory estimation for massive UEs, the LTE event streamer is modelled to produce several task units with events data for massive UEs. The task level modelled data structures are scheduled across Arm Cortex A15 based MPcore, with multiple threads. Finally, with massive UE live trajectory estimation, IMSI (International mobile subscriber identity) is used to maintain hidden markov requirements of particle filter functionality while maintaining load balance for 4 Arm A15 cores. This is proved by serial and parallel performance engineering. Future work is proposed for Decentralized task level scheduling with hash function for IMSI with extension of cores and Concentric circles method for AoA accuracy. / Mobiltelefoners positionering är välfungerande för positionslokalisering av mobiltelefoner när de rör sig från en plats till en annan. Lokaliseringstjänsterna inkluderar mobil positionering system som kan användas till en mängd olika kundbehovs tjänster som sökning av position, position i kartor, navigering, vägtransporters trafik managering och nödsituationssamtal med positionering. Mobil positions system (MPS) stödjer komplementär positions metoder för 2G, 3G och 4G/LTE (Long Term Evolution) nätverk. Mobiltelefoner är populärt känd som UE (User Equipment) inom LTE. En prototypmetod med verkliga rörelsers estimering för massiv UE i LTE nätverk har blivit föreslagen för detta examens arbete. RSRP (Reference Signal Received Power) värden och TA (Timing Advance) värden är del av LTE händelser för UE. Dessa specifika LTE event kan strömmas till ett system från eNodeB del av LTE, i realtid genom aktivering av mätningar på UEar i nätverk. AoA (Angel of Arrival) och TA värden är använt för att beräkna UEs position. AoA beräkningar är genomförda genom användandet av RSRP värden. Den kalkylerade UE positionen är filtrerad genom användande av Particle Filter (PF) för att estimera rörelsen. För att identifiera verkliga rörelser, beräkningar för massiva UEs, LTE event streamer är modulerad att producera flera uppgifts enheter med event data från massiva UEar. De tasks modulerade data strukturerna är planerade över Arm Cortex A15 baserade MPcore, med multipla trådar. Slutligen, med massiva UE verkliga rörelser, beräkningar med IMSI(International mobile subscriber identity) är använt av den Hidden Markov kraven i Particle Filter’s funktionalitet medans kravet att underhålla last balansen för 4 Arm A15 kärnor. Detta är utfört genom seriell och parallell prestanda teknik. Framtida arbeten för decentraliserade task nivå skedulering med hash funktion för IMSI med utökning av kärnor och Concentric circles metod för AoA noggrannhet.