101 research outputs found
Compressive Sensing and Its Applications in Automotive Radar Systems
Die Entwicklung in Richtung zu autonomem Fahren verspricht, kĂŒnftig einen sicheren
Verkehr ohne tödliche UnfÀlle zu ermöglichen, indem menschliche Fahrer vollstÀndig
ersetzt werden. Dadurch entfÀllt der Faktor des menschlichen Fehlers, der aus
MĂŒdigkeit, Unachtsamkeit oder Alkoholeinfluss resultiert. Um jedoch eine breite
Akzeptanz fĂŒr autonome Fahrzeuge zu erreichen und es somit eines Tages vollstĂ€ndig
umzusetzen, sind noch eine Vielzahl von Herausforderungen zu lösen. Da in einem
autonomen Fahrzeug kein menschlicher Fahrer mehr in NotfÀllen eingreifen kann,
mĂŒssen sich autonome Fahrzeuge auf leistungsfĂ€hige und robuste Sensorsysteme
verlassen können, um in kritischen Situationen auch unter widrigen Bedingungen
angemessen reagieren zu können. Daher ist die Entwicklung von Sensorsystemen
erforderlich, die fĂŒr FunktionalitĂ€ten jenseits der aktuellen advanced driver assistance
systems eingesetzt werden können. Dies resultiert in neuen Anforderungen, die erfĂŒllt
werden mĂŒssen, um sichere und zuverlĂ€ssige autonome Fahrzeuge zu realisieren, die
weder Fahrzeuginsassen noch Passanten gefÀhrden. Radarsysteme gehören zu den
SchlĂŒsselkomponenten unter der Vielzahl der verfĂŒgbaren Sensorsysteme, da sie im
Gegensatz zu visuellen Sensoren von widrigen Wetter- und Umgebungsbedingungen
kaum beeintrĂ€chtigt werden. DarĂŒber hinaus liefern Radarsysteme zusĂ€tzliche
Umgebungsinformationen wie Abstand, Winkel und relative Geschwindigkeit zwischen
Sensor und reflektierenden Zielen. Die vorliegende Dissertation deckt im Wesentlichen
zwei Hauptaspekte der Forschung und Entwicklung auf dem Gebiet der Radarsysteme
im Automobilbereich ab. Ein Aspekt ist die Steigerung der Effizienz und Robustheit
der Signalerfassung und -verarbeitung fĂŒr die Radarperzeption. Der andere Aspekt ist
die Beschleunigung der Validierung und Verifizierung von automated cyber-physical
systems, die parallel zum Automatisierungsgrad auch eine höhere KomplexitÀt
aufweisen.
Nach der Analyse zahlreicher möglicher Compressive Sensing Methoden, die im
Bereich Fahrzeugradarsysteme angewendet werden können, wird ein rauschmoduliertes
gepulstes Radarsystem vorgestellt, das kommerzielle Fahrzeugradarsysteme in
seiner Robustheit gegenĂŒber Rauschen ĂŒbertrifft. Die Nachteile anderer gepulster
Radarsysteme hinsichtlich des Signalerfassungsaufwands und der Laufzeit werden
durch die Verwendung eines Compressive Sensing-Signalerfassungs- und Rekonstruktionsverfahrens
in Kombination mit einer Rauschmodulation deutlich verringert.
Mit Compressive Sensing konnte der Aufwand fĂŒr die Signalerfassung um 70% reduziert
werden, wĂ€hrend gleichzeitig die Robustheit der Radarwahrnehmung auch fĂŒr signal-to-noise-ratio-Pegel nahe oder unter Null erreicht wird. Mit einem validierten
Radarsensormodell wurde das Rauschradarsystem emuliert und mit einem
kommerziellen Fahrzeugradarsystem verglichen. Datengetriebene Wettermodelle
wurden entwickelt und wÀhrend der Simulation angewendet, um die Radarleistung
unter widrigen Bedingungen zu bewerten. WĂ€hrend eine BesprĂŒhung mit Wasser die
RadomdÀmpfung um 10 dB erhöht und Spritzwasser sogar um 20 dB, ergibt sich die
eigentliche Begrenzung aus der Rauschzahl und Empfindlichkeit des EmpfÀngers. Es
konnte bewiesen werden, dass das vorgeschlagene Compressive Sensing Rauschradarsystem
mit einer zusÀtzlichen SignaldÀmpfung von bis zu 60 dB umgehen kann
und damit eine hohe Robustheit in ungĂŒnstigen Umwelt- und Wetterbedingungen
aufweist.
Neben der Robustheit wird auch die Interferenz berĂŒcksichtigt. Zum einen wird
die erhöhte Störfestigkeit des Störradarsystems nachgewiesen. Auf der anderen
Seite werden die Auswirkungen auf bestehende Fahrzeugradarsysteme bewertet und
Strategien zur Minderung der Auswirkungen vorgestellt.
Die Struktur der Arbeit ist folgende. Nach der EinfĂŒhrung der Grundlagen
und Methoden fĂŒr Fahrzeugradarsysteme werden die Theorie und Metriken hinter
Compressive Sensing gezeigt. DarĂŒber hinaus werden weitere Aspekte wie Umgebungsbedingungen,
unterschiedliche Radararchitekturen und Interferenz erlÀutert.
Der Stand der Technik gibt einen Ăberblick ĂŒber Compressive Sensing-AnsĂ€tze und
Implementierungen mit einem Fokus auf Radar. DarĂŒber hinaus werden Aspekte
von Fahrzeug- und Rauschradarsystemen behandelt. Der Hauptteil beginnt mit
der Vorstellung verschiedener AnsĂ€tze zur Nutzung von Compressive Sensing fĂŒr
Fahrzeugradarsysteme, die in der Lage sind, die Erfassung und Wahrnehmung von
Radarsignalen zu verbessern oder zu erweitern. AnschlieĂend wird der Fokus auf
ein Rauschradarsystem gelegt, das mit Compressive Sensing eine effiziente Signalerfassung
und -rekonstruktion ermöglicht. Es wurde mit verschiedenen Compressive
Sensing-Metriken analysiert und in einer Proof-of-Concept-Simulation bewertet. Mit
einer Emulation des Rauschradarsystems wurde das Potential der Compressive Sensing
Signalerfassung und -verarbeitung in einem realistischeren Szenario demonstriert.
Die Entwicklung und Validierung des zugrunde liegenden Sensormodells wird ebenso
dokumentiert wie die Entwicklung der datengetriebenen Wettermodelle. Nach der
Betrachtung von Interferenz und der Koexistenz des Rauschradars mit kommerziellen
Radarsystemen schlieĂt ein letztes Kapitel mit Schlussfolgerungen und einem
Ausblick die Arbeit ab.Developments towards autonomous driving promise to lead to safer traffic, where fatal
accidents can be avoided after making human drivers obsolete and hence removing
the factor of human error. However, to ensure the acceptance of automated driving
and make it a reality one day, still a huge amount of challenges need to be solved.
With having no human supervisors, automated vehicles have to rely on capable and
robust sensor systems to ensure adequate reactions in critical situations, even during
adverse conditions. Therefore, the development of sensor systems is required that
can be applied for functionalities beyond current advanced driver assistance systems.
New requirements need to be met in order to realize safe and reliable automated
vehicles that do not harm passersby.
Radar systems belong to the key components among the variety of sensor systems.
Other than visual sensors, radar is less vulnerable towards adverse weather and
environment conditions. In addition, radar provides complementary environment
information such as target distance, angular position or relative velocity, too. The
thesis ad hand covers basically two main aspects of research and development in the
field of automotive radar systems. One aspect is to increase efficiency and robustness
in signal acquisition and processing for radar perception. The other aspect is to
accelerate validation and verification of automated cyber-physical systems that
feature more complexity along with the level of automation.
After analyzing a variety of possible Compressive Sensing methods for automotive
radar systems, a noise modulated pulsed radar system is suggested in the thesis at
hand, which outperforms commercial automotive radar systems in its robustness
towards noise. Compared to other pulsed radar systems, their drawbacks regarding
signal acquisition effort and computation run time are resolved by using noise modulation
for implementing a Compressive Sensing signal acquisition and reconstruction
method. Using Compressive Sensing, the effort in signal acquisition was reduced by
70%, while obtaining a radar perception robustness even for signal-to-noise-ratio
levels close to or below zero. With a validated radar sensor model the noise radar
was emulated and compared to a commercial automotive radar system. Data-driven
weather models were developed and applied during simulation to evaluate radar performance
in adverse conditions. While water sprinkles increase radome attenuation
by 10 dB and splash water even by 20 dB, the actual limitation comes from noise
figure and sensitivity of the receiver. The additional signal attenuation that can be
handled by the proposed compressive sensing noise radar system proved to be even up to 60 dB, which ensures a high robustness of the receiver during adverse weather
and environment conditions.
Besides robustness, interference is also considered. On the one hand the increased
robustness towards interference of the noise radar system is demonstrated. On
the other hand, the impact on existing automotive radar systems is evaluated and
strategies to mitigate the impact are presented.
The structure of the thesis is the following. After introducing basic principles
and methods for automotive radar systems, the theory and metrics of Compressive
Sensing is presented. Furthermore some particular aspects are highlighted such as
environmental conditions, different radar architectures and interference. The state of
the art provides an overview on Compressive Sensing approaches and implementations
with focus on radar. In addition, it covers automotive radar and noise radar related
aspects. The main part starts with presenting different approaches on making use
of Compressive Sensing for automotive radar systems, that are capable of either
improving or extending radar signal acquisition and perception. Afterwards the focus
is put on a noise radar system that uses Compressive Sensing for an efficient signal
acquisition and reconstruction. It was analyzed using different Compressive Sensing
metrics and evaluated in a proof-of-concept simulation. With an emulation of the
noise radar system the feasibility of the Compressive Sensing signal acquisition and
processing was demonstrated in a more realistic scenario. The development and
validation of the underlying sensor model is documented as well as the development
of the data-driven weather models. After considering interference and co-existence
with commercial radar systems, a final chapter with conclusions and an outlook
completes the work
Sparse machine learning methods with applications in multivariate signal processing
This thesis details theoretical and empirical work that draws from two main subject areas: Machine
Learning (ML) and Digital Signal Processing (DSP). A unified general framework is given for the application
of sparse machine learning methods to multivariate signal processing. In particular, methods that
enforce sparsity will be employed for reasons of computational efficiency, regularisation, and compressibility.
The methods presented can be seen as modular building blocks that can be applied to a variety
of applications. Application specific prior knowledge can be used in various ways, resulting in a flexible
and powerful set of tools. The motivation for the methods is to be able to learn and generalise from a set
of multivariate signals.
In addition to testing on benchmark datasets, a series of empirical evaluations on real world
datasets were carried out. These included: the classification of musical genre from polyphonic audio
files; a study of how the sampling rate in a digital radar can be reduced through the use of Compressed
Sensing (CS); analysis of human perception of different modulations of musical key from
Electroencephalography (EEG) recordings; classification of genre of musical pieces to which a listener
is attending from Magnetoencephalography (MEG) brain recordings. These applications demonstrate
the efficacy of the framework and highlight interesting directions of future research
Recommended from our members
A Cognitive Radio Compressive Sensing Framework
With the proliferation of wireless devices and services, allied with further significant predicted growth, there is an ever increasing demand for higher transmission rates. This is especially challenging given the limited availability of radio spectrum, and is further exacerbated by a rigid licensing regulatory regime. Spectrum however, is largely underutilized and this has prompted regulators to promote the concept of opportunistic spectrum access. This allows unlicensed secondary users to use bands which are licensed to primary users, but are currently unoccupied, so leading to more efficient spectrum utilization.
A potentially attractive solution to this spectrum underutilisation problem is cognitive radio (CR) technology, which enables the identification and usage of vacant bands by continuously sensing the radio environment, though CR enforces stringent timing requirements and high sampling rates. Compressive sensing (CS) has emerged as a novel sampling paradigm, which provides the theoretical basis to resolve some of these issues, especially for signals exhibiting sparsity in some domain. For CR-related signals however, existing CS architectures such as the random demodulator and compressive multiplexer have limitations in regard to the signal types used, spectrum estimation methods applied, spectral band classification and a dependence on Fourier domain based sparsity.
This thesis presents a new generic CS framework which addresses these issues by specifically embracing three original scientific contributions: i) seamless embedding of the concept of precolouring into existing CS architectures to enhance signal sparsity for CR-related digital modulation schemes; ii) integration of the multitaper spectral estimator to improve sparsity in CR narrowband modulation schemes; and iii) exploiting sparsity in an alternative, non-Fourier (Walsh-Hadamard) domain to expand the applicable CR-related modulation schemes.
Critical analysis reveals the new CS framework provides a consistently superior and robust solution for the recovery of an extensive set of currently employed CR-type signals encountered in wireless communication standards. Significantly, the generic and portable nature of the framework affords the opportunity for further extensions into other CS architectures and sparsity domains
Measurement and modelling of spectrum occupancy
Based on the conception of spectrum sharing, cognitive Radio as a promising technology for optimizing utilization of the radio spectrum has emerged to revolutionize the next generation wireless communications industry. In order to adopt this technology, the current spectrum allocation strategy has to be reformed and the real spectrum occupancy information has to be systemically investigated. To assess the feasibility of cognitive radio technology, the statistical information of the present spectral occupancy needs to be examined thoroughly, which forms the basis of the spectrum occupancy project. We studied the 100-2500 MHz spectrum with the traditional radio monitoring systems whose technical details have been fully recorded in this thesis. In order to detect the frequency agile signals, a channel sounder, which is capable of scanning 300 MHz spectrum within 4 ms with multiple channel inputs, was used as a dedicated radio receiver in our measurements. The conclusion of the statistical information from the spectrum monitoring experiments shows that the spectrum occupancy range from 100-2500 MHz are low indeed in the measuring locations and period. The average occupancies for most bands are less than 20%. Especially, the average occupancies in the 1 GHz to 2.5GHz spectrum are less than 5%. Time series analysis was initially introduced in spectrum occupancy analysis as a tool to model spectrum occupancy variations with time. For instance, the time series Airline model fits well the GSM band occupancy data. In this thesis, generalized linear models were used as complementarily solutions to model occupancy data into other parameters such as signal amplitude. The validation of the direction of arrival algorithms (EM and SAGE) was verified with the anechoic chamber, by which we can determine the spectrum occupancy in space domain
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Ultrasound imaging as an effective method is widely used in medical diagnosis andNDT (non-destructive testing). In particular, ultrasound imaging plays an important role in medical diagnosis due to its safety, noninvasive, inexpensiveness and real-time compared with other medical imaging techniques. However, in general the ultrasound imaging has more speckles and is low definition than the MRI (magnetic resonance imaging) and X-ray CT (computerized tomography). Therefore, it is important to improve the ultrasound imaging quality. In this study, there are three newproposals. The first is the development of a high sensitivity transducer that utilizes piezoelectric charge directly for FET (field effect transistor) channel control. The second is a proposal of a method for estimating the distribution of small scatterers in living tissue using the empirical Bayes method. The third is a super-resolution imagingmethod of scatterers with strong reflection such as organ boundaries and blood vessel walls. The specific description of each chapter is as follows: Chapter 1: The fundamental characteristics and the main applications of ultrasound are discussed, then the advantages and drawbacks of medical ultrasound are high-lighted. Based on the drawbacks, motivations and objectives of this study are stated. Chapter 2: To overcome disadvantages of medical ultrasound, we advanced our studyin two directions: designing new transducer improves the acquisition modality itself, onthe other hand new signal processing improve the acquired echo data. Therefore, the conventional techniques related to the two directions are reviewed. Chapter 3: For high performance piezoelectric, a structure that enables direct coupling of a PZT (lead zirconate titanate) element to the gate of a MOSFET (metal-oxide semiconductor field-effect transistor) to provide a device called the PZT-FET that acts as an ultrasound receiver was proposed. The experimental analysis of the PZT-FET, in terms of its reception sensitivity, dynamic range and -6 dB reception bandwidth have been investigated. The proposed PZT-FET receiver offers high sensitivity, wide dynamic range performance when compared to the typical ultrasound transducer. Chapter 4: In medical ultrasound imaging, speckle patterns caused by reflection interference from small scatterers in living tissue are often suppressed by various methodologies. However, accurate imaging of small scatterers is important in diagnosis; therefore, we investigated influence of speckle pattern on ultrasound imaging by the empirical Bayesian learning. Since small scatterers are spatially correlated and thereby constitute a microstructure, we assume that scatterers are distributed according to the AR (auto regressive) model with unknown parameters. Under this assumption, the AR parameters are estimated by maximizing the marginal likelihood function, and the scatterers distribution is estimated as a MAP (maximum a posteriori) estimator. The performance of our method is evaluated by simulations and experiments. Through the results, we confirmed that the band limited echo has sufficient information of the AR parameters and the power spectrum of the echoes from the scatterers is properly extrapolated. Chapter 5: The medical ultrasound imaging of strong reflectance scatterers based on the MUSIC algorithm is the main subject of Chapter 5. Previously, we have proposed a super-resolution ultrasound imaging based on multiple TRs (transmissions/receptions) with different carrier frequencies called SCM (super resolution FM-chirp correlation method). In order to reduce the number of required TRs for the SCM, the method has been extended to the SA (synthetic aperture) version called SA-SCM. However, since super-resolution processing is performed for each line data obtained by the RBF (reception beam forming) in the SA-SCM, image discontinuities tend to occur in the lateral direction. Therefore, a new method called SCM-weighted SA is proposed, in this version the SCM is performed on each transducer element, and then the SCM result is used as the weight for RBF. The SCM-weighted SA can generate multiple B-mode images each of which corresponds to each carrier frequency, and the appropriate low frequency images among them have no grating lobes. For a further improvement, instead of simple averaging, the SCM applied to the result of the SCM-weighted SA for all frequencies again, which is called SCM-weighted SA-SCM. We evaluated the effectiveness of all the methods by simulations and experiments. From the results, it can be confirmed that the extension of the SCM framework can help ultrasound imaging reduce grating lobes, perform super-resolution and better SNR(signal-to-noise ratio). Chapter 6: A discussion of the overall content of the thesis as well as suggestions for further development together with the remaining problems are summarized.éŠéœć€§ćŠæ±äșŹ, 2019-03-25, ć棫ïŒć·„ćŠïŒéŠéœć€§ćŠæ±
Nonlinear models and algorithms for RF systems digital calibration
Focusing on the receiving side of a communication system, the current trend in pushing the digital domain ever more closer to the antenna sets heavy constraints on the accuracy and linearity of the analog front-end and the conversion devices. Moreover, mixed-signal implementations of Systems-on-Chip using nanoscale CMOS processes result in an overall poorer analog performance and a reduced yield. To cope with the impairments of the low performance analog section in this "dirty RF" scenario, two solutions exist: designing more complex analog processing architectures or to identify the errors and correct them in the digital domain using DSP algorithms. In the latter, constraints in the analog circuits' precision can be offloaded to a digital signal processor.
This thesis aims at the development of a methodology for the analysis, the modeling and the compensation of the analog impairments arising in different stages of a receiving chain using digital calibration techniques.
Both single and multiple channel architectures are addressed exploiting the capability of the calibration algorithm to homogenize all the channels' responses of a multi-channel system in addition to the compensation of nonlinearities in each response. The systems targeted for the application of digital post compensation are a pipeline ADC, a digital-IF sub-sampling receiver and a 4-channel TI-ADC.
The research focuses on post distortion methods using nonlinear dynamic models to approximate the post-inverse of the nonlinear system and to correct the distortions arising from static and dynamic errors. Volterra model is used due to its general approximation capabilities for the compensation of nonlinear systems with memory. Digital calibration is applied to a Sample and Hold and to a pipeline ADC simulated in the 45nm process, demonstrating high linearity improvement even with incomplete settling errors enabling the use of faster clock speeds.
An extended model based on the baseband Volterra series is proposed and applied to the compensation of a digital-IF sub-sampling receiver. This architecture envisages frequency selectivity carried out at IF by an active band-pass CMOS filter causing in-band and out-of-band nonlinear distortions. The improved performance of the proposed model is demonstrated with circuital simulations of a 10th-order band pass filter, realized using a five-stage Gm-C Biquad cascade, and validated using out-of-sample sinusoidal and QAM signals. The same technique is extended to an array receiver with mismatched channels' responses showing that digital calibration can compensate the loss of directivity and enhance the overall system SFDR.
An iterative backward pruning is applied to the Volterra models showing that complexity can be reduced without impacting linearity, obtaining state-of-the-art accuracy/complexity performance.
Calibration of Time-Interleaved ADCs, widely used in RF-to-digital wideband receivers, is carried out developing ad hoc models because the steep discontinuities generated by the imperfect canceling of aliasing would require a huge number of terms in a polynomial approximation. A closed-form solution is derived for a 4-channel TI-ADC affected by gain errors and timing skews solving the perfect reconstruction equations. A background calibration technique is presented based on cyclo-stationary filter banks architecture. Convergence speed and accuracy of the recursive algorithm are discussed and complexity reduction techniques are applied
Interference Mitigation and Localization Based on Time-Frequency Analysis for Navigation Satellite Systems
Interference Mitigation and Localization
Based on Time-Frequency Analysis for
Navigation Satellite SystemsNowadays, the operation of global navigation satellite systems (GNSS) is imperative across a multitude of applications worldwide. The increasing reliance on accurate positioning and timing information has made more serious than ever the consequences of possible service outages in the satellite navigation systems. Among others, interference is regarded as the primary threat to their operation. Due the recent proliferation of portable interferers, notably jammers, it has now become common for GNSS receivers to endure simultaneous attacks from multiple sources of interference, which are likely spatially distributed and transmit different modulations.
To the best knowledge of the author, the present dissertation is the first publication to investigate the use of the S-transform (ST) to devise countermeasures to interference. The original contributions in this context are mainly:
âą the formulation of a complexity-scalable ST implementable in real time as a
bank of filters;
âą a method for characterizing and localizing multiple in-car jammers through
interference snapshots that are collected by separate receivers and analysed
with a clever use of the ST;
âą a preliminary assessment of novel methods for mitigating generic interference
at the receiver end by means the ST and more computationally efficient variants of the transform.
Besides GNSSs, the countermeasures to interference proposed are equivalently applicable to protect any direct-sequence
spread spectrum (DS-SS) communication
- âŠ