8 research outputs found
Envelope and phase delays correction in an EER radio architecture
International audienceThis article deals with synchronization in the Envelope Elimination and Restoration (EER) type of transmitter architecture. To illustrate the performances of such solution, we choose to apply this architecture to a 64 carriers 16QAM modulated OFDM. We first introduce the problematic of the realisation of a highly linear transmitter.We then present the Envelope Elimination and Restoration solution and draw attention to its major weakness: a high sensitivity to desynchronization between the phase and envelope signal paths. To address this issue, we propose an adaptive synchronization algorithm relying on a feedback loop, a LeastMean Square formulation and involving an interpolation step. It enables the correction of delay mismatches and tracking of possible variations. We demonstrate that the quality of the interpolator has a direct impact on Error Vector Magnitude (EVM) value and output spectrum. Implementation details are provided along with an analysis of the behaviour and performances of the method. We present HPADS and Matlab simulation results and then focus on the enhancement of the transmitter performances using the proposed algorithm
Time Delay Estimation Using LASSO (Least Absolute Selection and Shrinkage Operator)
In decades, many researchers have studied the time delay estimation (TDE) method for the signals in the two different receivers. The channel estimation based TDE is one of the typical TDE methods. The channel estimation based TDE models the time delay between two receiving signals as an impulse response in a channel between two receivers. In general the impulse response becomes sparse. However, most conventional TDE algorithms cannot have utilized the sparsity. In this paper, we propose a TDE method taking the sparsity into consideration. The performance comparison shows that the proposed algorithm improves the estimation accuracy by 10 dB in the white gaussian source. In addition, even in the colored source, the proposed algorithm doesn't show the estimation threshold effect
Adaptive explicit time delay, frequency estimations in communications systems
Ph.DDOCTOR OF PHILOSOPH
Robust aircraft trajectory optimization under meteorological uncertainty
Mención Internacional en el título de doctorThe Air Traffic Management (ATM) system in the busiest airspaces in the world
is currently being overhauled to deal with multiple capacity, socioeconomic, and environmental
challenges. One major pillar of this process is the shift towards a concept
of operations centered on aircraft trajectories (called Trajectory-Based Operations or
TBO in Europe) instead of rigid airspace structures. However, its successful implementation
(and, thus, the realization of the associated improvements in ATM performance)
rests on appropriate understanding and management of uncertainty. Due to its complex
socio-technical structure, the design and operations of the ATM system are heavily impacted
by uncertainty, proceeding from multiple sources and propagating through the
interconnections between its subsystems.
One major source of ATM uncertainty is weather. Due to its nonlinear and chaotic
nature, a number of meteorological phenomena of interest cannot be forecasted with
complete accuracy at arbitrary lead times, which leads to uncertainty or disruption in
individual air and ground operations that propagates to all ATM processes. Therefore,
in order to achieve the goals of SESAR and similar programs, it is necessary to deal
with meteorological uncertainty at multiple scales, from the trajectory prediction and
planning processes to flow and traffic management operations.
This thesis addresses the problem of single-aircraft flight planning considering two
important sources of meteorological uncertainty: wind prediction error and convective
activity. As the actual wind field deviates from its forecast, the actual trajectory will
diverge in time from the planned trajectory, generating uncertainty in arrival times,
sector entry and exit times, and fuel burn. Convective activity also impacts trajectory
predictability, as it leads pilots to deviate from their planned route, creating challenging
situations for controllers. In this work, we aim to develop algorithms and methods
for aircraft trajectory optimization that are able to integrate information about the
uncertainty in these meteorological phenomena into the flight planning process at both
pre-tactical (before departure) and tactical horizons (while the aircraft is airborne), in
order to generate more efficient and predictable trajectories.
To that end, we frame flight planning as an optimal control problem, modeling the
motion of the aircraft with a point-mass model and the BADA performance model. Optimal
control methods represent a flexible and general approach that has a long history
of success in the aerospace field. As a numerical scheme, we use direct methods, which
can deal with nonlinear systems of moderate and high-dimensional state spaces in a
computationally manageable way. Nevertheless, while this framework is well-developed
in the context of deterministic problems, the techniques for the solution of practical optimal control problems under uncertainty are not as mature, and the methods proposed
in the literature are not applicable to the flight planning problem as it is now
understood.
The first contribution of this thesis addresses this challenge by introducing a framework
for the solution of general nonlinear optimal control problems under parametric
uncertainty. It is based on an ensemble trajectory scheme, where the trajectories of the
system under multiple scenarios are considered simultaneously within the same dynamical
system and the uncertain optimal control problem is turned into a large conventional
optimal control problem that can be then solved by standard, well-studied direct methods
in optimal control. We then employ this approach to solve the robust flight plan
optimization problem at the planning horizon. In order to model uncertainty in the
wind and estimating the probability of convective conditions, we employ Ensemble Prediction
System (EPS) forecasts, which are composed by multiple predictions instead of
a single deterministic one. The resulting method can be used to optimize flight plans for
maximum expected efficiency according to the cost structure of the airline; additionally,
predictability and exposure to convection can be incorporated as additional objectives.
The inherent tradeoffs between these objectives can be assessed with this methodology.
The second part of this thesis presents a solution for the rerouting of aircraft in
uncertain convective weather scenarios at the tactical horizon. The uncertain motion of
convective weather cells is represented with a stochastic model that has been developed
from the output of a deterministic satellite-based nowcast product, Rapidly Developing
Thunderstorms (RDT). A numerical optimal control framework, based on the pointmass
model with the addition of turn dynamics, is employed for optimizing efficiency
and predictability of the proposed trajectories in the presence of uncertainty about
the future evolution of the storm. Finally, the optimization process is initialized by a
randomized heuristic procedure that generates multiple starting points. The combined
framework is able to explore and as exploit the space of solution trajectories in order to
provide the pilot or the air traffic controller with a set of different suggested avoidance
trajectories, as well as information about their expected cost and risk.
The proposed methods are tested on example scenarios based on real data, showing
how different user priorities lead to different flight plans and what tradeoffs are then
present. These examples demonstrate that the solutions described in this thesis are
adequate for the problems that have been formulated. In this way, the flight planning
process can be enhanced to increase the efficiency and predictability of individual aircraft
trajectories, which would lead to higher predictability levels of the ATM system and thus
improvements in multiple performance indicators.El sistema de gestión del tráfico aéreo (Air Traffic Management, ATM) en los espacios
aéreos más congestionados del mundo está siendo reformado para lidiar con múltiples
desafíos socioeconómicos, medioambientales y de capacidad. Un pilar de este proceso es
el gradual reemplazo de las estructuras rígidas de navegación, basadas en aerovías y waypoints,
hacia las operaciones basadas en trayectorias. No obstante, la implementación
exitosa de este concepto y la realización de las ganancias esperadas en rendimiento ATM
requiere entender y gestionar apropiadamente la incertidumbre. Debido a su compleja
estructura socio-técnica, el diseño y operaciones del sistema ATM se encuentran marcadamente
influidos por la incertidumbre, que procede de múltiples fuentes y se propaga
por las interacciones entre subsistemas y operadores humanos.
Uno de los principales focos de incertidumbre en ATM es la meteorología. Debido a su
naturaleza no-linear y caótica, muchos fenómenos de interés no pueden ser pronosticados
con completa precisión en cualquier horizonte temporal, lo que crea disrupción en las
operaciones en aire y tierra que se propaga a otros procesos de ATM. Por lo tanto,
para lograr los objetivos de SESAR e iniciativas análogas, es imprescindible tener en
cuenta la incertidumbre en múltiples escalas espaciotemporales, desde la predicción de
trayectorias hasta la planificación de flujos y tráfico.
Esta tesis aborda el problema de la planificación de vuelo de aeronaves individuales
considerando dos fuentes importantes de incertidumbre meteorológica: el error en la
predicción del viento y la actividad convectiva. Conforme la realización del viento se
desvía de su previsión, la trayectoria real se desviará temporalmente de la planificada, lo
que implica incertidumbre en tiempos de llegada a sectores y aeropuertos y en consumo
de combustible. La actividad convectiva también tiene un impacto en la predictibilidad
de las trayectorias, puesto que obliga a los pilotos a desviarse de sus planes de vuelo
para evitarla, cambiado así la situación de tráfico. En este trabajo, buscamos desarrollar
métodos y algoritmos para la optimización de trayectorias que puedan integrar
información sobre la incertidumbre en estos fenómenos meteorológicos en el proceso de
diseño de planes de vuelo en horizontes de planificación (antes del despegue) y tácticos
(durante el vuelo), con el objetivo de generar trayectorias más eficientes y predecibles.
Con este fin, formulamos la planificación de vuelo como un problema de control
óptimo, modelando la dinámica del avión con un modelo de masa puntual y el modelo
de rendimiento BADA. El control óptimo es un marco flexible y general con un largo
historial de éxito en el campo de la ingeniería aeroespacial. Como método numérico,
empleamos métodos directos, que son capaces de manejar sistemas dinámicos de alta
dimensión con costes computacionales moderados. No obstante, si bien esta metodología es madura en contextos deterministas, la solución de problemas prácticas de control
óptimo bajo incertidumbre en la literatura no está tan desarrollada, y los métodos
propuestos en la literatura no son aplicables al problema de interés.
La primera contribución de esta tesis hace frente a este reto mediante la introducción
de un marco numérico para la resolución de problemas generales de control óptimo
no-lineal bajo incertidumbre paramétrica. El núcleo de este método es un esquema de
conjunto de trayectorias, en el que las trayectorias del sistema dinámico bajo múltiples
escenarios son consideradas de forma simultánea, y el problema de control óptimo bajo
incertidumbre es así transformado en un problema convencional que puede ser tratado
mediante métodos existentes en control óptimo. A continuación, empleamos este método
para resolver el problema de la planificación de vuelo robusta. La incertidumbre en el
viento y la probabilidad de ocurrencia de condiciones convectivas son modeladas mediante
el uso de previsiones de conjunto o ensemble, compuestas por múltiples predicciones
en lugar de una única previsión determinista. Este método puede ser empleado para
maximizar la eficiencia esperada de los planes de vuelo de acuerdo a la estructura de
costes de la aerolínea; además, la predictibilidad de la trayectoria y la exposición a la
convección pueden ser incorporadas como objetivos adicionales. El trade-off entre estos
objetivos puede ser evaluado mediante la metodología propuesta.
La segunda parte de la tesis presenta una solución para reconducir aviones en escenarios
tormentosos en un horizonte táctico. La evolución de las células convectivas es
representada con un modelo estocástico basado en las proyecciones de Rapidly Developing
Thunderstorms (RDT), un sistema determinista basado en imágenes de satélite.
Este modelo es empleado por un método de control óptimo numérico, basado en un
modelo de masa puntual en el que se modela la dinámica de viraje, con el objetivo de
maximizar la eficiencia y predictibilidad de la trayectoria en presencia de incertidumbre
sobre la evolución futura de las tormentas. Finalmente, el proceso de optimizatión es
inicializado por un método heurístico aleatorizado que genera múltiples puntos de inicio
para las iteraciones del optimizador. Esta combinación permite explorar y explotar el
espacio de trayectorias solución para proporcionar al piloto o al controlador un conjunto
de trayectorias propuestas, así como información útil sobre su coste y el riesgo asociado.
Los métodos propuestos son probados en escenarios de ejemplo basados en datos
reales, ilustrando las diferentes opciones disponibles de acuerdo a las prioridades del
planificador y demostrando que las soluciones descritas en esta tesis son adecuadas para
los problemas que se han formulado. De este modo, es posible enriquecer el proceso de
planificación de vuelo para incrementar la eficiencia y predictibilidad de las trayectorias
individuales, lo que contribuiría a mejoras en el rendimiento del sistema ATM.These works have been financially supported by Universidad Carlos III de Madrid
through a PIF scholarship; by Eurocontrol, through the HALA! Research Network grant
10-220210-C2; by the Spanish Ministry of Economy and Competitiveness (MINECO)'s
R&D program, through the OptMet project (TRA2014-58413-C2-2-R); and by the European
Commission's SESAR Horizon 2020 program, through the TBO-Met project
(grant number 699294).Programa de Doctorado en Mecánica de Fluidos por la Universidad Carlos III de Madrid; la Universidad de Jaén; la Universidad de Zaragoza; la Universidad Nacional de Educación a Distancia; la Universidad Politécnica de Madrid y la Universidad Rovira iPresidente: Damián Rivas Rivas.- Secretario: Xavier Prats Menéndez.- Vocal: Benavar Sridha
Efficient geometric sound propagation using visibility culling
Simulating propagation of sound can improve the sense of realism in interactive applications such as video games and can lead to better designs in engineering applications such as architectural acoustics. In this thesis, we present geometric sound propagation techniques which are faster than prior methods and map well to upcoming parallel multi-core CPUs. We model specular reflections by using the image-source method and model finite-edge diffraction by using the well-known Biot-Tolstoy-Medwin (BTM) model. We accelerate the computation of specular reflections by applying novel visibility algorithms, FastV and AD-Frustum, which compute visibility from a point. We accelerate finite-edge diffraction modeling by applying a novel visibility algorithm which computes visibility from a region. Our visibility algorithms are based on frustum tracing and exploit recent advances in fast ray-hierarchy intersections, data-parallel computations, and scalable, multi-core algorithms. The AD-Frustum algorithm adapts its computation to the scene complexity and allows small errors in computing specular reflection paths for higher computational efficiency. FastV and our visibility algorithm from a region are general, object-space, conservative visibility algorithms that together significantly reduce the number of image sources compared to other techniques while preserving the same accuracy. Our geometric propagation algorithms are an order of magnitude faster than prior approaches for modeling specular reflections and two to ten times faster for modeling finite-edge diffraction. Our algorithms are interactive, scale almost linearly on multi-core CPUs, and can handle large, complex, and dynamic scenes. We also compare the accuracy of our sound propagation algorithms with other methods. Once sound propagation is performed, it is desirable to listen to the propagated sound in interactive and engineering applications. We can generate smooth, artifact-free output audio signals by applying efficient audio-processing algorithms. We also present the first efficient audio-processing algorithm for scenarios with simultaneously moving source and moving receiver (MS-MR) which incurs less than 25% overhead compared to static source and moving receiver (SS-MR) or moving source and static receiver (MS-SR) scenario
Adaptive subsample time delay estimation using Lagrange interpolators
This letter addresses the problem of on-line subsample time delay estimation of narrowband signals of known center frequency. A new form of the Lagrange interpolator filter is presented in this letter which is incorporated into the explicit time delay estimator (ETDE) method. Simulations show that ETDE with the filter modulated to the signal center frequency significantly outperforms conventional ETDE