228 research outputs found
New Approach of Indoor and Outdoor Localization Systems
Accurate determination of the mobile position constitutes the basis of many new applications. This book provides a detailed account of wireless systems for positioning, signal processing, radio localization techniques (Time Difference Of Arrival), performances evaluation, and localization applications. The first section is dedicated to Satellite systems for positioning like GPS, GNSS. The second section addresses the localization applications using the wireless sensor networks. Some techniques are introduced for localization systems, especially for indoor positioning, such as Ultra Wide Band (UWB), WIFI. The last section is dedicated to Coupled GPS and other sensors. Some results of simulations, implementation and tests are given to help readers grasp the presented techniques. This is an ideal book for students, PhD students, academics and engineers in the field of Communication, localization & Signal Processing, especially in indoor and outdoor localization domains
Signal processing architectures for automotive high-resolution MIMO radar systems
To date, the digital signal processing for an automotive radar sensor has been handled in an efficient way by general purpose signal processors and microcontrollers. However, increasing resolution requirements for automated driving on the one hand, as well as rapidly growing numbers of manufactured sensors on the other hand, can provoke a paradigm change in the near future. The design and development of highly specialized hardware accelerators could become a viable option - at least for the most demanding processing steps with data rates of several gigabits per second.
In this work, application-specific signal processing architectures for future high-resolution multiple-input and multiple-output (MIMO) radar sensors are designed, implemented, investigated and optimized. A focus is set on real-time performance such that even sophisticated algorithms can be computed sufficiently fast. The full processing chain from the received baseband signals to a list of detections is considered, comprising three major steps: Spectrum analysis, target detection and direction of arrival estimation.
The developed architectures are further implemented on a field-programmable gate array (FPGA) and important measurements like resource consumption, power dissipation or data throughput are evaluated and compared with other examples from literature. A substantial dataset, based on more than 3600 different parametrizations and variants, has been established with the help of a model-based design space exploration and is provided as part of this work. Finally, an experimental radar sensor has been built and is used under real-world conditions to verify the effectiveness of the proposed signal processing architectures.Bisher wurde die digitale Signalverarbeitung für automobile Radarsensoren auf eine effiziente Art und Weise von universell verwendbaren Mikroprozessoren bewältigt. Jedoch können steigende Anforderungen an das Auflösungsvermögen für hochautomatisiertes Fahren einerseits, sowie schnell wachsende Stückzahlen produzierter Sensoren andererseits, einen Paradigmenwechsel in naher Zukunft bewirken. Die Entwicklung von hochgradig spezialisierten Hardwarebeschleunigern könnte sich als eine praktikable Alternative etablieren - zumindest für die anspruchsvollsten Rechenschritte mit Datenraten von mehreren Gigabits pro Sekunde.
In dieser Arbeit werden anwendungsspezifische Signalverarbeitungsarchitekturen für zukünftige, hochauflösende, MIMO Radarsensoren entworfen, realisiert, untersucht und optimiert. Der Fokus liegt dabei stets auf der Echtzeitfähigkeit, sodass selbst anspruchsvolle Algorithmen in einer ausreichend kurzen Zeit berechnet werden können. Die komplette Signalverarbeitungskette, beginnend von den empfangenen Signalen im Basisband bis hin zu einer Liste von Detektion, wird in dieser Arbeit behandelt. Die Kette gliedert sich im Wesentlichen in drei größere Teilschritte: Spektralanalyse, Zieldetektion und Winkelschätzung.
Des Weiteren werden die entwickelten Architekturen auf einem FPGA implementiert und wichtige Kennzahlen wie Ressourcenverbrauch, Stromverbrauch oder Datendurchsatz ausgewertet und mit anderen Beispielen aus der Literatur verglichen. Ein umfangreicher Datensatz, welcher mehr als 3600 verschiedene Parametrisierungen und Varianten beinhaltet, wurde mit Hilfe einer modellbasierten Entwurfsraumexploration erstellt und ist in dieser Arbeit enthalten. Schließlich wurde ein experimenteller Radarsensor aufgebaut und dazu benutzt, die entworfenen Signalverarbeitungsarchitekturen unter realen Umgebungsbedingungen zu verifizieren
The University Defence Research Collaboration In Signal Processing
This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations.
The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour
3D reconstruction and motion estimation using forward looking sonar
Autonomous Underwater Vehicles (AUVs) are increasingly used in different domains
including archaeology, oil and gas industry, coral reef monitoring, harbour’s security,
and mine countermeasure missions. As electromagnetic signals do not penetrate
underwater environment, GPS signals cannot be used for AUV navigation, and optical
cameras have very short range underwater which limits their use in most underwater
environments.
Motion estimation for AUVs is a critical requirement for successful vehicle recovery
and meaningful data collection. Classical inertial sensors, usually used for AUV motion
estimation, suffer from large drift error. On the other hand, accurate inertial sensors are
very expensive which limits their deployment to costly AUVs. Furthermore, acoustic
positioning systems (APS) used for AUV navigation require costly installation and
calibration. Moreover, they have poor performance in terms of the inferred resolution.
Underwater 3D imaging is another challenge in AUV industry as 3D information is
increasingly demanded to accomplish different AUV missions. Different systems have
been proposed for underwater 3D imaging, such as planar-array sonar and T-configured
3D sonar. While the former features good resolution in general, it is very expensive and
requires huge computational power, the later is cheaper implementation but requires
long time for full 3D scan even in short ranges.
In this thesis, we aim to tackle AUV motion estimation and underwater 3D imaging by
proposing relatively affordable methodologies and study different parameters affecting
their performance. We introduce a new motion estimation framework for AUVs which
relies on the successive acoustic images to infer AUV ego-motion. Also, we propose an
Acoustic Stereo Imaging (ASI) system for underwater 3D reconstruction based on
forward looking sonars; the proposed system features cheaper implementation than
planar array sonars and solves the delay problem in T configured 3D sonars
Sound Processing for Autonomous Driving
Nowadays, a variety of intelligent systems for autonomous driving have been developed, which have already shown a very high level of capability. One of the prerequisites for autonomous driving is an accurate and reliable representation of the environment around the vehicle. Current systems rely on cameras, RADAR, and LiDAR to capture the visual environment and to locate and track other traffic participants. Human drivers, in addition to vision, have hearing and use a lot of auditory information to understand the environment in addition to visual cues. In this thesis, we present the sound signal processing system for auditory based environment representation.
Sound propagation is less dependent on occlusion than all other types of sensors and in some situations is less sensitive to different types of weather conditions such as snow, ice, fog or rain. Various audio processing algorithms provide the detection and classification of different audio signals specific to certain types of vehicles, as well as localization.
First, the ambient sound is classified into fourteen major categories consisting of traffic objects and actions performed. Additionally, the classification of three specific types of emergency vehicles sirens is provided. Secondly, each object is localized using a combined localization algorithm based on time difference of arrival and amplitude. The system is evaluated on real data with a focus on reliable detection and accurate localization of emergency vehicles. On the third stage the possibility of visualizing the sound source on the image from the autonomous vehicle camera system is provided. For this purpose, a method for camera to microphones calibration has been developed.
The presented approaches and methods have great potential to increase the accuracy of environment perception and, consequently, to improve the reliability and safety of autonomous driving systems in general
Estudo de formas de onda e conceção de algoritmos para operação conjunta de sistemas de comunicação e radar
The focus of this thesis is the processing of signals and design of algorithms
that can be used to enable radar functions in communications systems.
Orthogonal frequency division multiplexing (OFDM) is a popular multicarrier
modulation waveform in communication systems. As a wideband
signal, OFDM improves resolution and enables spectral efficiency in radar
systems, while also improving detection performance thanks to its inherent
frequency diversity. This thesis aims to use multicarrier waveforms for radar
systems, to enable the simultaneous operation of radar and communication
functions on the same device. The thesis is divided in two parts. The first
part, studies the adaptation and application of other multicarrier waveforms
to radar functions. At the present time many studies have been carried out
to jointly use the OFDM signal for communication and radar functions, but
other waveforms have shown to be possible candidates for communication
applications. Therefore, studies on the evaluation of the application of these
same signals to radar functions are necessary. In this thesis, to demonstrate
that other multicarrier waveforms can overcome the OFDM waveform
in radar/communication (RadCom) systems, we propose the adaptation of
the filter bank multicarrier (FBMC), generalized frequency division multiplexing
(GFDM) and universal filtering multicarrier (UFMC) waveforms for radar
functions. These alternative waveforms were compared performance-wise
regarding achievable target parameter estimation performance, amount of
residual background noise in the radar image, impact of intersystem interference
and flexibility of parameterization. In the second part of the thesis,
signal processing techniques are explored to solve some of the limitations
of the use of multicarrier waveforms for RadCom systems. Radar systems
based on OFDM are promising candidates for future intelligent transport networks.
Exploring the dual functionality enabled by OFDM, we presents cooperative
methods for high-resolution delay-Doppler and direction-of-arrival
estimation. High-resolution parameter estimation is an important requirement
for automotive radar systems, especially in multi-target scenarios that
require reliable target separation performance. By exploring the cooperation
between vehicles, the studies presented in this thesis also enable the distributed
tracking of targets. The result is a highly accurate multi-target tracking
across the entire cooperative vehicle network, leading to improvements
in transport reliability and safety.O foco desta tese é o processamento de sinais e desenvolvimento de algoritmos
que podem ser utilizados para a habilitar a função de radar nos sistemas
de comunicação. OFDM (Orthogonal Frequency Division Multiplexing)
é uma forma de onda com modulação multi-portadora, popular em sistemas
de comunicação. Para sistemas de radar, O OFDM melhora a resolução e
fornece eficiência espectral, além disso sua diversidade de frequências melhora
o desempenho na detecção do radar. Essa tese tem como objetivo
utilizar formas de onda multi-portadoras para sistemas de radar, possibilitando
a operação simultânea de funções de radar e de comunicação num
mesmo dispositivo. A tese esta dividida em duas partes. Na primeira parte
da tese são realizados estudos da adaptabilidade de outras formas de onda
multi-portadora para funções de radar. Nos dias atuais, muitos estudos sobre
o uso do sinal OFDM para funções de comunicação e radar vêm sendo
realizados, no entanto, outras formas de onda mostram-se possíveis candidatas
a aplicações em sistemas de comunicação, e assim, avaliações para
funções de sistema de radar se tornam necessárias. Nesta tese, com a
intenção de demonstrar que formas de onda multi-portadoras alternativas
podem superar o OFDM nos sistemas de Radar/comunicação (RadCom),
propomos a adaptação das seguintes formas de onda: FBMC (Filter Bank
Multicarrier); GFDM (Generalized Frequency Division Multiplexing); e UFMC
(Universal Filtering Multicarrier) para funções de radar. Também produzimos
uma análise de desempenho dessas formas de onda sobre o aspecto
da estimativa de parâmetros-alvo, ruído de fundo, interferência entre sistemas
e parametrização do sistema. Na segunda parte da tese serão explorados
técnicas de processamento de sinal de forma a solucionar algumas
das limitações do uso de formas de ondas multi-portadora para sistemas
RadCom. Os sistemas de radar baseados no OFDM são candidatos
promissores para futuras redes de transporte inteligentes, porque combinam
funções de estimativa de alvo com funções de rede de comunicação
em um único sistema. Explorando a funcionalidade dupla habilitada pelo
OFDM, nesta tese, apresentamos métodos cooperativos de alta resolução
para estimar o posição, velocidade e direção dos alvos. A estimativa de
parâmetros de alta resolução é um requisito importante para sistemas de
radar automotivo, especialmente em cenários de múltiplos alvos que exigem
melhor desempenho de separação de alvos. Ao explorar a cooperação entre
veículos, os estudos apresentados nesta tese também permitem o rastreamento
distribuído de alvos. O resultado é um rastreamento multi-alvo altamente
preciso em toda a rede de veículos cooperativos, levando a melhorias
na confiabilidade e segurança do transporte.Programa Doutoral em Telecomunicaçõe
Optimization and Communication in UAV Networks
UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects
Modelling, Simulation and Data Analysis in Acoustical Problems
Modelling and simulation in acoustics is currently gaining importance. In fact, with the development and improvement of innovative computational techniques and with the growing need for predictive models, an impressive boost has been observed in several research and application areas, such as noise control, indoor acoustics, and industrial applications. This led us to the proposal of a special issue about “Modelling, Simulation and Data Analysis in Acoustical Problems”, as we believe in the importance of these topics in modern acoustics’ studies. In total, 81 papers were submitted and 33 of them were published, with an acceptance rate of 37.5%. According to the number of papers submitted, it can be affirmed that this is a trending topic in the scientific and academic community and this special issue will try to provide a future reference for the research that will be developed in coming years
Wireless communication, sensing, and REM: A security perspective
The diverse requirements of next-generation communication systems necessitate awareness, flexibility, and intelligence as essential building blocks of future wireless networks. The awareness can be obtained from the radio signals in the environment using wireless sensing and radio environment mapping (REM) methods. This is, however, accompanied by threats such as eavesdropping, manipulation, and disruption posed by malicious attackers. To this end, this work analyzes the wireless sensing and radio environment awareness mechanisms, highlighting their vulnerabilities and provides solutions for mitigating them. As an example, the different threats to REM and its consequences in a vehicular communication scenario are described. Furthermore, the use of REM for securing communications is discussed and future directions regarding sensing/REM security are highlighted
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