Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Controlled time of arrival feasibility analysis

Previous research studies and operational trials have shown that using the airborne Required Time of Arrival (RTA) function, an aircraft can individually achieve an assigned time to a metering or merge point accurately. This study goes a step further and investigates the application of RTA to a real sequence of arriving aircraft into Melbourne Australia. Assuming that the actual arrival times were Controlled Time of Arrivals (CTAs) assigned to each aircraft, the study examines if the airborne RTA solution would work. Three scenarios were compared: a baseline scenario being the actual flown trajectories in a two hour time-span into Melbourne, a scenario in which the sequential landing slot times of the baseline scenario were assigned as CTAs and a third scenario in which the landing slots could be freely redistributed to the inbound traffic as CTAs. The research found that pressure on the terminal area would sometimes require aircraft to lose more time than possible through the RTA capability. Using linear holding as an additional measure to absorb extensive delays, up to 500NM (5%) of total track reduction and 1300kg (3%) of total fuel consumption could be saved in the scenario with landing slots freely distributed as CTAs, compared to the baseline scenario. Assigning CTAs in an arrival sequence requires the ground system to have an accurate trajectory predictor to propose additional delay measures (path stretching, linear holding) if necessary. Reducing the achievable time window of the aircraft to add control margin to the RTA function, had a negative impact and increased the amount of intervention other than speed control required to solve the sequence. It was concluded that the RTA capability is not a complete solution but merely a tool to assist in managing the increasing complexity of air traffic.

Average performance analysis of circular and hyperbolic geolocation

A comparative performance analysis of four geolocation methods in terms of their theoretical root mean square positioning errors is provided. Comparison is established in two different ways: strict and average. In the strict type, methods are examined for a particular geometric configuration of base stations(BSs) with respect to mobile position, which determines a givennoise profile affecting the respective time-of-arrival (TOA) or timedifference-of-arrival (TDOA) estimates. In the average type, methodsare evaluated in terms of the expected covariance matrix ofthe position error over an ensemble of random geometries, so thatcomparison is geometry independent. Exact semianalytical equationsand associated lower bounds (depending solely on the noiseprofile) are obtained for the average covariance matrix of the positionerror in terms of the so-called information matrix specific toeach geolocation method. Statistical channel models inferred fromfield trials are used to define realistic prior probabilities for therandom geometries. A final evaluation provides extensive resultsrelating the expected position error to channel model parametersand the number of base stations.

Multi-node TOA-DOA cooperative LOS-NLOS localization : enabling high accuracy and reliability

This dissertation investigates high performance cooperative localization in wireless environments based on multi-node time-of-arrival (TOA) and direction-of-arrival (DOA) estimations in line-of-sight (LOS) and non-LOS (NLOS) scenarios. Here, two categories of nodes are assumed: base nodes (BNs) and target nodes (TNs). BNs are equipped with antenna arrays and capable of estimating TOA (range) and DOA (angle). TNs are equipped with Omni-directional antennas and communicate with BNs to allow BNs to localize TNs; thus, the proposed localization is maintained by BNs and TNs cooperation. First, a LOS localization method is proposed, which is based on semi-distributed multi-node TOA-DOA fusion. The proposed technique is applicable to mobile ad-hoc networks (MANETs). We assume LOS is available between BNs and TNs. One BN is selected as the reference BN, and other nodes are localized in the coordinates of the reference BN. Each BN can localize TNs located in its coverage area independently. In addition, a TN might be localized by multiple BNs. High performance localization is attainable via multi-node TOA-DOA fusion. The complexity of the semi-distributed multi-node TOA-DOA fusion is low because the total computational load is distributed across all BNs. To evaluate the localization accuracy of the proposed method, we compare the proposed method with global positioning system (GPS) aided TOA (DOA) fusion, which are applicable to MANETs. The comparison criterion is the localization circular error probability (CEP). The results confirm that the proposed method is suitable for moderate scale MANETs, while GPS-aided TOA fusion is suitable for large scale MANETs. Usually, TOA and DOA of TNs are periodically estimated by BNs. Thus, Kalman filter (KF) is integrated with multi-node TOA-DOA fusion to further improve its performance. The integration of KF and multi-node TOA-DOA fusion is compared with extended-KF (EKF) when it is applied to multiple TOA-DOA estimations made by multiple BNs. The comparison depicts that it is stable (no divergence takes place) and its accuracy is slightly lower than that of the EKF, if the EKF converges. However, the EKF may diverge while the integration of KF and multi-node TOA-DOA fusion does not; thus, the reliability of the proposed method is higher. In addition, the computational complexity of the integration of KF and multi-node TOA-DOA fusion is much lower than that of EKF. In wireless environments, LOS might be obstructed. This degrades the localization reliability. Antenna arrays installed at each BN is incorporated to allow each BN to identify NLOS scenarios independently. Here, a single BN measures the phase difference across two antenna elements using a synchronized bi-receiver system, and maps it into wireless channel’s K-factor. The larger K is, the more likely the channel would be a LOS one. Next, the K-factor is incorporated to identify NLOS scenarios. The performance of this system is characterized in terms of probability of LOS and NLOS identification. The latency of the method is small. Finally, a multi-node NLOS identification and localization method is proposed to improve localization reliability. In this case, multiple BNs engage in the process of NLOS identification, shared reflectors determination and localization, and NLOS TN localization. In NLOS scenarios, when there are three or more shared reflectors, those reflectors are localized via DOA fusion, and then a TN is localized via TOA fusion based on the localization of shared reflectors.

Multi-antenna non-line-of-sight identification techniques for target localization in mobile ad-hoc networks

Target localization has a wide range of military and civilian applications in wireless mobile networks. Examples include battle-field surveillance, emergency 911 (E911), traffc alert, habitat monitoring, resource allocation, routing, and disaster mitigation. Basic localization techniques include time-of-arrival (TOA), direction-of-arrival (DOA) and received-signal strength (RSS) estimation. Techniques that are proposed based on TOA and DOA are very sensitive to the availability of Line-of-sight (LOS) which is the direct path between the transmitter and the receiver. If LOS is not available, TOA and DOA estimation errors create a large localization error. In order to reduce NLOS localization error, NLOS identifcation, mitigation, and localization techniques have been proposed. This research investigates NLOS identifcation for multiple antennas radio systems. The techniques proposed in the literature mainly use one antenna element to enable NLOS identifcation. When a single antenna is utilized, limited features of the wireless channel can be exploited to identify NLOS situations. However, in DOA-based wireless localization systems, multiple antenna elements are available. In addition, multiple antenna technology has been adopted in many widely used wireless systems such as wireless LAN 802.11n and WiMAX 802.16e which are good candidates for localization based services. In this work, the potential of spatial channel information for high performance NLOS identifcation is investigated. Considering narrowband multiple antenna wireless systems, two xvNLOS identifcation techniques are proposed. Here, the implementation of spatial correlation of channel coeffcients across antenna elements as a metric for NLOS identifcation is proposed. In order to obtain the spatial correlation, a new multi-input multi-output (MIMO) channel model based on rough surface theory is proposed. This model can be used to compute the spatial correlation between the antenna pair separated by any distance. In addition, a new NLOS identifcation technique that exploits the statistics of phase difference across two antenna elements is proposed. This technique assumes the phases received across two antenna elements are uncorrelated. This assumption is validated based on the well-known circular and elliptic scattering models. Next, it is proved that the channel Rician K-factor is a function of the phase difference variance. Exploiting Rician K-factor, techniques to identify NLOS scenarios are proposed. Considering wideband multiple antenna wireless systems which use MIMO-orthogonal frequency division multiplexing (OFDM) signaling, space-time-frequency channel correlation is exploited to attain NLOS identifcation in time-varying, frequency-selective and spaceselective radio channels. Novel NLOS identi?cation measures based on space, time and frequency channel correlation are proposed and their performances are evaluated. These measures represent a better NLOS identifcation performance compared to those that only use space, time or frequency.

High precision range estimation techniques for multi-path wireless environments

Range estimation is the core of many positioning systems such as radar, and Wireless Local Positioning Systems (WLPS). The estimation of range is achieved by estimating Time-of-Arrival (TOA). TOA represents the signal propagation delay between a transmitter and a receiver. Thus, error in TOA estimation causes degradation in range estimation performance. In wireless environments, noise, multipath, and limited bandwidth reduce TOA estimation performance. TOA estimation algorithms that are designed for wireless environments aim to improve the TOA estimation performance by mitigating the effect of closely spaced paths in practical (positive) signal-to-noise ratio (SNR) regions. Limited bandwidth avoids the discrimination of closely spaced paths. This reduces TOA estimation performance. TOA estimation methods are evaluated as a function of SNR, bandwidth, and the number of reflections in multipath wireless environments, as well as their complexity. In this research, a TOA estimation technique based on Blind signal Separation (BSS) is proposed. This frequency domain method estimates TOA in wireless multipath environments for a given signal bandwidth. The structure of the proposed technique is presented and its complexity and performance are theoretically evaluated. It is depicted that the proposed method is not sensitive to SNR, number of reflections, and bandwidth. In general, as bandwidth increases, TOA estimation performance improves. However, spectrum is the most valuable resource in wireless systems and usually a large portion of spectrum to support high performance TOA estimation is not available. In addition, the radio frequency (RF) components of wideband systems suffer from high cost and complexity. Thus, a novel, multiband positioning structure is proposed. The proposed technique uses the available (non-contiguous) bands to support high performance TOA estimation. This system incorporates the capabilities of cognitive radio (CR) systems to sense the available spectrum (also called white spaces) and to incorporate white spaces for high-performance localization. First, contiguous bands that are divided into several non-equal, narrow sub-bands that possess the same SNR are concatenated to attain an accuracy corresponding to the equivalent full band. Two radio architectures are proposed and investigated: the signal is transmitted over available spectrum either simultaneously (parallel concatenation) or sequentially (serial concatenation). Low complexity radio designs that handle the concatenation process sequentially and in parallel are introduced. Different TOA estimation algorithms that are applicable to multiband scenarios are studied and their performance is theoretically evaluated and compared to simulations. Next, the results are extended to non-contiguous, non-equal sub-bands with the same SNR. These are more realistic assumptions in practical systems. The performance and complexity of the proposed technique is investigated as well. This study’s results show that selecting bandwidth, center frequency, and SNR levels for each sub-band can adapt positioning accuracy.

Structural health monitoring of storage tanks

In this thesis, Ph.D candidate presents a compact sensor node (SN) designed for long-term and real-time acoustic emission (AE) monitoring of above ground storage tanks (ASTs). Each SN exploits up to three inexpensive low-frequency sensors based on piezoelectric diaphragms for effective leakage detection, and it is capable by means of built-in Digital Signal Processing functionalities to process the acquired time waveforms extracting the AE features usually required by testing protocols. Alternatively, capability to plug three high frequency AE sensors to a SN for corrosion simulated phenomena detection is envisaged and demonstrated. Another innovative aspect that the Ph.D candidate presents in this work is an alternative mathematical model of corrosion location on the bottom of the AST. This approach implies considering the three-dimensional localization model versus the two-dimensional commonly used according to the literature. This approach is aimed at significant optimization in the number of sensors in relation to the standard approach for solving localization problems as well as to allow filtering the false AE events related to the condensate droplets from AST ceiling. The technological implementation of this concept required the solution of a number of technical problems, such as the precise time of arrival (ToA) signal estimation, vertical localization of the AE source and multilaration solution that were discussed in detail in this work. To validate the developed prototype, several experimental campaigns were organized that included the simulation of target phenomena both in laboratory conditions and on a real water storage tank. The presented test results demonstrate the successful application of the developed AE system both for simulated leaks and for corrosion processes on the tank bottom. Mathematical and technological algorithms for localization and characterization of AE signals implemented during the development of the prototype are also confirmed by the test results.

Neural Network based Non Orthogonal Random Access for 6G NTN-IoT

Pervasive and distributed Internet of Things (IoT) devices demand ubiquitous coverage beyond No-man’s land. To satisfy plethora of IoT devices with resilient connectivity, Non-Terrestrial Networks (NTN) will be pivotal to assist and complement terrestrial systems. In a massiveMTC scenario over NTN, characterized by sporadic uplink data reports, all the terminals within a satellite beam shall be served during the short visibility window of the flying platform, thus generating congestion due to simultaneous access attempts of IoT devices on the same radio resource. The more terminals collide, the more average-time it takes to complete an access which is due to the decreased number of successful attempts caused by Back-off commands of legacy methods. A possible countermeasure is represented by Non-Orthogonal Multiple Access scheme, which requires the knowledge of the number of superimposed NPRACH preambles. This work addresses this problem by proposing a Neural Network (NN) algorithm to cope with the uncoordinated random access performed by a prodigious number of Narrowband-IoT devices. Our proposed method classifies the number of colliding users, and for each estimates the Time of Arrival (ToA). The performance assessment, under Line of Sight (LoS) and Non-LoS conditions in sub-urban environments with two different satellite configurations, shows significant benefits of the proposed NN algorithm with respect to traditional methods for the ToA estimation.

Readout architectures for hybrid pixel detector readout chips

The original contribution of this thesis to knowledge are novel digital readout architectures for hybrid pixel readout chips. The thesis presents asynchronous bus-based architecture, a data-node based column architecture and a network-based pixel matrix architecture for data transportation. It is shown that the data-node architecture achieves readout efficiency 99% with half the output rate as a bus-based system. The network-based solution avoids “broken” columns due to some manufacturing errors, and it distributes internal data traffic more evenly across the pixel matrix than column-based architectures. An improvement of > 10% to the efficiency is achieved with uniform and non-uniform hit occupancies. Architectural design has been done using transaction level modeling (TLM) and sequential high-level design techniques for reducing the design and simulation time. It has been possible to simulate tens of column and full chip architectures using the high-level techniques. A decrease of > 10 in run-time is observed using these techniques compared to register transfer level (RTL) design technique. Reduction of 50% for lines-of-code (LoC) for the high-level models compared to the RTL description has been achieved. Two architectures are then demonstrated in two hybrid pixel readout chips. The first chip, Timepix3 has been designed for the Medipix3 collaboration. According to the measurements, it consumes < 1 W/cm^2. It also delivers up to 40 Mhits/s/cm^2 with 10-bit time-over-threshold (ToT) and 18-bit time-of-arrival (ToA) of 1.5625 ns. The chip uses a token-arbitrated, asynchronous two-phase handshake column bus for internal data transfer. It has also been successfully used in a multi-chip particle tracking telescope. The second chip, VeloPix, is a readout chip being designed for the upgrade of Vertex Locator (VELO) of the LHCb experiment at CERN. Based on the simulations, it consumes < 1.5 W/cm^2 while delivering up to 320 Mpackets/s/cm^2, each packet containing up to 8 pixels. VeloPix uses a node-based data fabric for achieving throughput of 13.3 Mpackets/s from the column to the EoC. By combining Monte Carlo physics data with high-level simulations, it has been demonstrated that the architecture meets requirements of the VELO (260 Mpackets/s/cm^2 with efficiency of 99%).

Diseño de algoritmos de guerra electrónica y radar para su implementación en sistemas de tiempo real

Esta tesis se centra en el estudio y desarrollo de algoritmos de guerra electrónica {electronic warfare, EW) y radar para su implementación en sistemas de tiempo real. La llegada de los sistemas de radio, radar y navegación al terreno militar llevó al desarrollo de tecnologías para combatirlos. Así, el objetivo de los sistemas de guerra electrónica es el control del espectro electomagnético. Una de la funciones de la guerra electrónica es la inteligencia de señales {signals intelligence, SIGINT), cuya labor es detectar, almacenar, analizar, clasificar y localizar la procedencia de todo tipo de señales presentes en el espectro. El subsistema de inteligencia de señales dedicado a las señales radar es la inteligencia electrónica {electronic intelligence, ELINT). Un sistema de tiempo real es aquel cuyo factor de mérito depende tanto del resultado proporcionado como del tiempo en que se da dicho resultado. Los sistemas radar y de guerra electrónica tienen que proporcionar información lo más rápido posible y de forma continua, por lo que pueden encuadrarse dentro de los sistemas de tiempo real. La introducción de restricciones de tiempo real implica un proceso de realimentación entre el diseño del algoritmo y su implementación en plataformas “hardware”. Las restricciones de tiempo real son dos: latencia y área de la implementación. En esta tesis, todos los algoritmos presentados se han implementado en plataformas del tipo field programmable gate array (FPGA), ya que presentan un buen compromiso entre velocidad, coste total, consumo y reconfigurabilidad. La primera parte de la tesis está centrada en el estudio de diferentes subsistemas de un equipo ELINT: detección de señales mediante un detector canalizado, extracción de los parámetros de pulsos radar, clasificación de modulaciones y localization pasiva. La transformada discreta de Fourier {discrete Fourier transform, DFT) es un detector y estimador de frecuencia quasi-óptimo para señales de banda estrecha en presencia de ruido blanco. El desarrollo de algoritmos eficientes para el cálculo de la DFT, conocidos como fast Fourier transform (FFT), han situado a la FFT como el algoritmo más utilizado para la detección de señales de banda estrecha con requisitos de tiempo real. Así, se ha diseñado e implementado un algoritmo de detección y análisis espectral para su implementación en tiempo real. Los parámetros más característicos de un pulso radar son su tiempo de llegada y anchura de pulso. Se ha diseñado e implementado un algoritmo capaz de extraer dichos parámetros. Este algoritmo se puede utilizar con varios propósitos: realizar un reconocimiento genérico del radar que transmite dicha señal, localizar la posición de dicho radar o bien puede utilizarse como la parte de preprocesado de un clasificador automático de modulaciones. La clasificación automática de modulaciones es extremadamente complicada en entornos no cooperativos. Un clasificador automático de modulaciones se divide en dos partes: preprocesado y el algoritmo de clasificación. Los algoritmos de clasificación basados en parámetros representativos calculan diferentes estadísticos de la señal de entrada y la clasifican procesando dichos estadísticos. Los algoritmos de localization pueden dividirse en dos tipos: triangulación y sistemas cuadráticos. En los algoritmos basados en triangulación, la posición se estima mediante la intersección de las rectas proporcionadas por la dirección de llegada de la señal. En cambio, en los sistemas cuadráticos, la posición se estima mediante la intersección de superficies con igual diferencia en el tiempo de llegada (time difference of arrival, TDOA) o diferencia en la frecuencia de llegada (frequency difference of arrival, FDOA). Aunque sólo se ha implementado la estimación del TDOA y FDOA mediante la diferencia de tiempos de llegada y diferencia de frecuencias, se presentan estudios exhaustivos sobre los diferentes algoritmos para la estimación del TDOA, FDOA y localización pasiva mediante TDOA-FDOA. La segunda parte de la tesis está dedicada al diseño e implementación filtros discretos de respuesta finita (finite impulse response, FIR) para dos aplicaciones radar: phased array de banda ancha mediante filtros retardadores (true-time delay, TTD) y la mejora del alcance de un radar sin modificar el “hardware” existente para que la solución sea de bajo coste. La operación de un phased array de banda ancha mediante desfasadores no es factible ya que el retardo temporal no puede aproximarse mediante un desfase. La solución adoptada e implementada consiste en sustituir los desfasadores por filtros digitales con retardo programable. El máximo alcance de un radar depende de la relación señal a ruido promedio en el receptor. La relación señal a ruido depende a su vez de la energía de señal transmitida, potencia multiplicado por la anchura de pulso. Cualquier cambio hardware que se realice conlleva un alto coste. La solución que se propone es utilizar una técnica de compresión de pulsos, consistente en introducir una modulación interna a la señal, desacoplando alcance y resolución. ABSTRACT This thesis is focused on the study and development of electronic warfare (EW) and radar algorithms for real-time implementation. The arrival of radar, radio and navigation systems to the military sphere led to the development of technologies to fight them. Therefore, the objective of EW systems is the control of the electromagnetic spectrum. Signals Intelligence (SIGINT) is one of the EW functions, whose mission is to detect, collect, analyze, classify and locate all kind of electromagnetic emissions. Electronic intelligence (ELINT) is the SIGINT subsystem that is devoted to radar signals. A real-time system is the one whose correctness depends not only on the provided result but also on the time in which this result is obtained. Radar and EW systems must provide information as fast as possible on a continuous basis and they can be defined as real-time systems. The introduction of real-time constraints implies a feedback process between the design of the algorithms and their hardware implementation. Moreover, a real-time constraint consists of two parameters: Latency and area of the implementation. All the algorithms in this thesis have been implemented on field programmable gate array (FPGAs) platforms, presenting a trade-off among performance, cost, power consumption and reconfigurability. The first part of the thesis is related to the study of different key subsystems of an ELINT equipment: Signal detection with channelized receivers, pulse parameter extraction, modulation classification for radar signals and passive location algorithms. The discrete Fourier transform (DFT) is a nearly optimal detector and frequency estimator for narrow-band signals buried in white noise. The introduction of fast algorithms to calculate the DFT, known as FFT, reduces the complexity and the processing time of the DFT computation. These properties have placed the FFT as one the most conventional methods for narrow-band signal detection for real-time applications. An algorithm for real-time spectral analysis for user-defined bandwidth, instantaneous dynamic range and resolution is presented. The most characteristic parameters of a pulsed signal are its time of arrival (TOA) and the pulse width (PW). The estimation of these basic parameters is a fundamental task in an ELINT equipment. A basic pulse parameter extractor (PPE) that is able to estimate all these parameters is designed and implemented. The PPE may be useful to perform a generic radar recognition process, perform an emitter location technique and can be used as the preprocessing part of an automatic modulation classifier (AMC). Modulation classification is a difficult task in a non-cooperative environment. An AMC consists of two parts: Signal preprocessing and the classification algorithm itself. Featurebased algorithms obtain different characteristics or features of the input signals. Once these features are extracted, the classification is carried out by processing these features. A feature based-AMC for pulsed radar signals with real-time requirements is studied, designed and implemented. Emitter passive location techniques can be divided into two classes: Triangulation systems, in which the emitter location is estimated with the intersection of the different lines of bearing created from the estimated directions of arrival, and quadratic position-fixing systems, in which the position is estimated through the intersection of iso-time difference of arrival (TDOA) or iso-frequency difference of arrival (FDOA) quadratic surfaces. Although TDOA and FDOA are only implemented with time of arrival and frequency differences, different algorithms for TDOA, FDOA and position estimation are studied and analyzed. The second part is dedicated to FIR filter design and implementation for two different radar applications: Wideband phased arrays with true-time delay (TTD) filters and the range improvement of an operative radar with no hardware changes to minimize costs. Wideband operation of phased arrays is unfeasible because time delays cannot be approximated by phase shifts. The presented solution is based on the substitution of the phase shifters by FIR discrete delay filters. The maximum range of a radar depends on the averaged signal to noise ratio (SNR) at the receiver. Among other factors, the SNR depends on the transmitted signal energy that is power times pulse width. Any possible hardware change implies high costs. The proposed solution lies in the use of a signal processing technique known as pulse compression, which consists of introducing an internal modulation within the pulse width, decoupling range and resolution.

Localización y seguimiento de aeronaves mediante sistemas de multilateración de área extensa

El continuo crecimiento de la demanda del transporte aéreo, junto con los nuevos escenarios de intervención militar, están obligando a una optimización en el uso del espacio aéreo. De este modo, la UE y los EEUU (a través de SESAR y NextGen respectivamente) han asentado las bases para una nueva gestión del tráfico aéreo (ATM). Con ello, se pretende aumentar la capacidad de aeropuertos y rutas aéreas, otorgando mayor flexibilidad al uso del espacio aéreo sin comprometer la seguridad de los usuarios. Desde un punto de vista puramente técnico, la clave de este cambio de modelo está en el conocimiento de la posición de cada aeronave en cada instante. En este sentido, la tendencia en ATM es el uso de ADS-B como fuente principal de posicionamiento. Sin embargo, debido a que este sistema está basado en la difusión de la posición obtenida a través de GPS, es necesario un sistema de seguimiento independiente. Actualmente, la intención es migrar del radar secundario de vigilancia (SSR) a la multilateración de área extensa (WAM), con el fin de mejorar la integridad de la posición para aplicaciones en ruta. Aprovechando el rápido despliegue de ADS-B, se pretende reutilizar sus estaciones base para WAM. Cada estación base que recibe el mensaje ADS-B de la aeronave envía conjuntamente la medida del tiempo de llegada (TOA) de dicho mensaje al centro de tráfico aéreo. La posición de la aeronave se obtiene mediante multilateración, cuya técnica consiste en utilizar las medidas de TOA de un mismo mensaje ADS-B obtenidas en las distintas estaciones base. El objetivo es estimar la posición de cada aeronave con la mayor precisión posible. Para poder diseñar el sistema que permite alcanzar este objetivo, son dos los aspectos básicos a estudiar. Por una parte, la identificación y posterior caracterización de los errores (tanto sistemáticos como aleatorios) que afectan a la medida de TOA. Por otra parte, es necesario el estudio de los sistemas de seguimiento, basados en versiones sofisticadas del filtro de Kalman (IMM, UKF). Una vez establecidos estos dos pilares, la presente tesis doctoral propone un sistema que permite efectuar el seguimiento de las aeronaves, corrigiendo los efectos de las principales distorsiones que afectan a la medida de TOA: la refracción troposférica y el error de sincronismo. La mejora en la precisión de la localización ha sido evaluada mediante simulación de escenarios hipotéticos. ABSTRACT The ever-growing demand in the air transportation and the new military intervention scenarios, are generating a need to optimize the use of the airspace. This way, the EU and the USA (through SESAR and NextGen respectively) have set the ground to overhaul the current air traffic management. The intention is to enhance the capacity of airports and air routes, providing greater flexibility in the use of airspace without jeopardizing the security of the end-users. From a technical perspective, the key for this change lies in the knowledge of the aircraft position. The trend in Air Traffic Management (ATM) is to rely on ADS-B as the main source for aircraft positioning. However, this system is based on the aircraft’s self-declaration of its own (often GPS-based) navigation solution. It is therefore necessary to have an independent surveillance system. Nowadays, the intention is to gradually migrate from Secondary Surveillance Radar (SSR) towards Wide Area Multilateration (WAM) in order to enhance surveillance integrity for en-route applications. Given the fast deployment of ADS-B, the aim is to use its base stations for WAM. Each station sends the Time of Arrival (TOA) of the received ADS-B messages to the air traffic center (ATC). The aircraft position is obtained through multilateration, using the TOA of the same message measured by each station. The aim is to accurately estimate the position of each aircraft. Knowledge from two key areas has to be gathered prior to designing such a system. It is necessary to identify and then characterize the errors (both systematic and random) affecting the TOA measurements. The second element is the study of tracking systems based on sophisticated versions of the Kalman filtering (e.g. IMM, UKF). Based on this knowledge, the main contribution of this Ph.D. is an aircraft tracking system that corrects the effects of the main errors involved in the TOA measurement: tropospheric refraction and synchronization issues. Performance gains in positioning accuracy have been assessed by simulating hypothetical WAM scenarios.

HIGH PERFORMANCE, LOW COST SUBSPACE DECOMPOSITION AND POLYNOMIAL ROOTING FOR REAL TIME DIRECTION OF ARRIVAL ESTIMATION: ANALYSIS AND IMPLEMENTATION

This thesis develops high performance real-time signal processing modules for direction of arrival (DOA) estimation for localization systems. It proposes highly parallel algorithms for performing subspace decomposition and polynomial rooting, which are otherwise traditionally implemented using sequential algorithms. The proposed algorithms address the emerging need for real-time localization for a wide range of applications. As the antenna array size increases, the complexity of signal processing algorithms increases, making it increasingly difficult to satisfy the real-time constraints. This thesis addresses real-time implementation by proposing parallel algorithms, that maintain considerable improvement over traditional algorithms, especially for systems with larger number of antenna array elements. Singular value decomposition (SVD) and polynomial rooting are two computationally complex steps and act as the bottleneck to achieving real-time performance. The proposed algorithms are suitable for implementation on field programmable gated arrays (FPGAs), single instruction multiple data (SIMD) hardware or application specific integrated chips (ASICs), which offer large number of processing elements that can be exploited for parallel processing. The designs proposed in this thesis are modular, easily expandable and easy to implement. Firstly, this thesis proposes a fast converging SVD algorithm. The proposed method reduces the number of iterations it takes to converge to correct singular values, thus achieving closer to real-time performance. A general algorithm and a modular system design are provided making it easy for designers to replicate and extend the design to larger matrix sizes. Moreover, the method is highly parallel, which can be exploited in various hardware platforms mentioned earlier. A fixed point implementation of proposed SVD algorithm is presented. The FPGA design is pipelined to the maximum extent to increase the maximum achievable frequency of operation. The system was developed with the objective of achieving high throughput. Various modern cores available in FPGAs were used to maximize the performance and details of these modules are presented in detail. Finally, a parallel polynomial rooting technique based on Newton’s method applicable exclusively to root-MUSIC polynomials is proposed. Unique characteristics of root-MUSIC polynomial’s complex dynamics were exploited to derive this polynomial rooting method. The technique exhibits parallelism and converges to the desired root within fixed number of iterations, making this suitable for polynomial rooting of large degree polynomials. We believe this is the first time that complex dynamics of root-MUSIC polynomial were analyzed to propose an algorithm. In all, the thesis addresses two major bottlenecks in a direction of arrival estimation system, by providing simple, high throughput, parallel algorithms.

Retinal fluorescein contrast arrival time of young patients with the hepatosplenic form of the Schistosomiasis mansoni

Schistosoma mansoni is responsible for lesions that can alter the hemodinamic of the portal venous circulation, lung arterial and venous sistemic systems. Therefore, hemodinamic changes in the ocular circulation of mansonic schistosomotic patients with portal hypertension and hepatofugal venous blood flow is also probable. The purpose of this study was to determine the fluorescein contrast arrival time at the retina of young patients with the hepatosplenic form of schistosomiasis, clinically and surgically treated. The control group included 36 non schistosomotic patients, mean age of 17.3 years, and the case group was represented by 25 schistosomotic patients, mean age of 18.2 years, who were cared for at The University Hospital (Federal University of Pernambuco, Brazil), from 1990 to 2001. They underwent digital angiofluoresceinography and were evaluated for the contrast arrival time at the early retinal venous phase of the exam. Both groups were ophthalmologically examined at the same hospital (Altino Ventura Foundation, Recife, Brazil), using the same technique. There was retardation of the retinal contrast arrival time equal or more than 70 sec in the eyes of three schistosomotic patients (12%) and in none of the control group, however, the mean contrast arrival time between the two groups were not statistically different. These findings lend support to the hypothesis that there could be a delay of the eye venous blood flow drainage.