50 research outputs found
OFDM Synthetic Aperture Radar Imaging with Sufficient Cyclic Prefix
The existing linear frequency modulated (LFM) (or step frequency) and random
noise synthetic aperture radar (SAR) systems may correspond to the frequency
hopping (FH) and direct sequence (DS) spread spectrum systems in the past
second and third generation wireless communications. Similar to the current and
future wireless communications generations, in this paper, we propose OFDM SAR
imaging, where a sufficient cyclic prefix (CP) is added to each OFDM pulse. The
sufficient CP insertion converts an inter-symbol interference (ISI) channel
from multipaths into multiple ISI-free subchannels as the key in a wireless
communications system, and analogously, it provides an inter-range-cell
interference (IRCI) free (high range resolution) SAR image in a SAR system. The
sufficient CP insertion along with our newly proposed SAR imaging algorithm
particularly for the OFDM signals also differentiates this paper from all the
existing studies in the literature on OFDM radar signal processing. Simulation
results are presented to illustrate the high range resolution performance of
our proposed CP based OFDM SAR imaging algorithm.Comment: This version has been accepted by IEEE Transactions on Geoscience and
Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing 201
Joint waveform and guidance control optimisation for target rendezvous
The algorithm developed in this paper jointly selects the optimal transmitted waveform and the control input so that a radar sensor on a moving platform with linear dynamics can reach a target by minimising a predefined cost. The cost proposed in this paper accounts for the energy of the transmitted radar signal, the energy of the platform control input and the relative position error between the platform and the target, which is a function of the waveform design and control input. Similarly to the Linear Quadratic Gaussian (LQG) control problem, we demonstrate that the optimal solution satisfies the separation principle between filtering and optimisation and, therefore, the optimum can be found analytically. The performance of the proposed solution is assessed with a set of simulations for a pulsed Doppler radar transmitting linearly frequency modulated chirps. Results show the effectiveness of the proposed approach for optimal waveform design and optimal guidance control
Monostatic Airborne Synthetic Aperture Radar Using Commercial WiMAX Transceivers In the License-exempt Spectrum
The past half-century witnessed an evolution of synthetic aperture radar (SAR). Boosted by digital signal processing (DSP), a variety of SAR imaging algorithms have been developed, in which the wavenumber domain algorithm is mature for airborne SAR and independent of signal waveforms. Apart from the algorithm development, there is a growing interest in how to acquire the raw data of targets’ echoes before the DSP for SAR imaging in a cost-effective way. For the data acquisition, various studies over the past 15 years have shed light on utilizing the signal generated from the ubiquitous broadband wireless technology – orthogonal frequency division multiplexing (OFDM). However, the purpose of this thesis is to enable commercial OFDM-based wireless systems to work as an airborne SAR sensor.
The unlicensed devices of Worldwide interoperability for Microwave Access (WiMAX) are the first option, owing to their accessibility, similarity and economy. This dissertation first demonstrates the feasibility of applying WiMAX to SAR by discussing their similar features. Despite the similarities they share, the compatibility of the two technologies is undermined by a series of problems resulted from WiMAX transceiver mechanisms and industrial rules for radiated power. In order to directly apply commercial WiMAX base station transceivers in unlicensed band to airborne SAR application, we propose a radio-frequency (RF) front design together with a signal processing means. To be specific, a double-pole, double-throw (DPDT) switch is inserted between an antenna and two WiMAX transceivers for generating pulsed signal. By simulations, the transmitted power of the SAR sensor is lower than 0dBm, while its imaging range can be over 10km for targets with relatively large radar cross section (RCS), such as a ship. Its range resolution is 9.6m whereas its cross-range resolution is finer than 1m. Equipped with the multi-mode, this SAR sensor is further enhanced to satisfy the requirements of diversified SAR applications. For example, the width of the scan-mode SAR’s range swath is 2.1km, over five times the width of other modes. Vital developed Matlab code is given in Appendix D, and its correctness is shown by comparing with the image of chirped SAR.
To summarize, the significance of this dissertation is to propose, for the first time, a design of directly leveraging commercial OFDM-based systems for airborne SAR imaging. Compared with existing designs of airborne SAR, it is a promising low-cost solution
Adaptive waveform design for SAR in a crowded spectrum
This thesis concerns the development of an adaptive waveform design scheme for synthetic
aperture radar (SAR) to support its operation in the increasingly crowded radio
frequency (RF) spectrum, focusing on mitigating the effects of external RF interference.
The RF spectrum is a finite resource and the rapid expansion of the telecommunications
industry has seen radar users face a significant restriction in the range of available
operational frequencies. This crowded spectrum scenario leads to increased likelihood
of RF interference either due to energy leakage from neighbouring spectral users or
from unlicensed transmitters.
SAR is a wide bandwidth radar imaging mode which exploits the motion of the radar
platform to form an image using multiple one dimensional profiles of the scene of interest
known as the range profile. Due to its wideband nature, SAR is particularly vulnerable
to RF interference which causes image impairments and overall reduction in quality.
Altering the approach for radar energy transmission across the RF spectrum is now
imperative to continue effective operation.
Adaptive waveforms have recently become feasible for implementation and offer the
much needed flexibility in the choice and control over radar transmission. However,
there is a critically small processing time frame between waveform reception and transmission,
which necessitates the use of computationally efficient processing algorithms
to use adaptivity effectively.
This simulation-based study provides a first look at adaptive waveform design for SAR
to mitigate the detrimental effects of RF interference on a pulse-to-pulse basis. Standard
SAR systems rely on a fixed waveform processing format on reception which restricts its
potential to reap the benefits of adaptive waveform design. Firstly, to support waveform
design for SAR, system identification techniques are applied to construct an alternative
receive processing method which allows flexibility in waveform type. This leads to the
main contribution of the thesis which is the formation of an adaptive spectral waveform
design scheme. A computationally efficient closed-form expression for the waveform spectrum that minimizes the error in the estimate of the SAR range profile on a pulse to pulse basis is
derived. The range profile and the spectrum of the interference are estimated at each
pulse. The interference estimate is then used to redesign the proceeding waveform for
estimation of the range profile at the next radar platform position. The solution necessitates
that the energy is spread across the spectrum such that it competes with the
interferer. The scenario where the waveform admits gaps in the spectrum in order to
mitigate the effects of the interference is also detailed and is the secondary major thesis
contribution. A series of test SAR images demonstrate the efficacy of these techniques
and yield reduced interference effects compared to the standard SAR waveform
Investigation of Non-coherent Discrete Target Range Estimation Techniques for High-precision Location
Ranging is an essential and crucial task for radar systems. How to solve the range-detection problem effectively and precisely is massively important. Meanwhile, unambiguity and high resolution are the points of interest as well. Coherent and non-coherent techniques can be applied to achieve range estimation, and both of them have advantages and disadvantages. Coherent estimates offer higher precision but are more vulnerable to noise and clutter and phase wrap errors, particularly in a complex or harsh environment, while the non-coherent approaches are simpler but provide lower precision. With the purpose of mitigating inaccuracy and perturbation in range estimation, miscellaneous techniques are employed to achieve optimally precise detection. Numerous elegant processing solutions stemming from non-coherent estimate are now introduced into the coherent realm, and vice versa. This thesis describes two non-coherent ranging estimate techniques with novel algorithms to mitigate the instinct deficit of non-coherent ranging approaches. One technique is based on peak detection and realised by Kth-order Polynomial Interpolation, while another is based on Z-transform and realised by Most-likelihood Chirp Z-transform. A two-stage approach for the fine ranging estimate is applied to the Discrete Fourier transform domain of both algorithms. An N-point Discrete Fourier transform is implemented to attain a coarse estimation; an accurate process around the point of interest determined in the first stage is conducted. For KPI technique, it interpolates around the peak of Discrete Fourier transform profiles of the chirp signal to achieve accurate interpolation and optimum precision. For Most-likelihood Chirp Z-transform technique, the Chirp Z-transform accurately implements the periodogram where only a narrow band spectrum is processed. Furthermore, the concept of most-likelihood estimator is introduced to combine with Chirp Z-transform to acquire better ranging performance. Cramer-Rao lower bound is presented to evaluate the performance of these two techniques from the perspective of statistical signal processing. Mathematical derivation, simulation modelling, theoretical analysis and experimental validation are conducted to assess technique performance. Further research will be pushed forward to algorithm optimisation and system development of a location system using non-coherent techniques and make a comparison to a coherent approach
Compressive Sensing and Its Applications in Automotive Radar Systems
Die Entwicklung in Richtung zu autonomem Fahren verspricht, künftig einen sicheren
Verkehr ohne tödliche Unfälle zu ermöglichen, indem menschliche Fahrer vollständig
ersetzt werden. Dadurch entfällt der Faktor des menschlichen Fehlers, der aus
Müdigkeit, Unachtsamkeit oder Alkoholeinfluss resultiert. Um jedoch eine breite
Akzeptanz für autonome Fahrzeuge zu erreichen und es somit eines Tages vollständig
umzusetzen, sind noch eine Vielzahl von Herausforderungen zu lösen. Da in einem
autonomen Fahrzeug kein menschlicher Fahrer mehr in Notfällen eingreifen kann,
müssen sich autonome Fahrzeuge auf leistungsfähige 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 für Funktionalitäten jenseits der aktuellen advanced driver assistance
systems eingesetzt werden können. Dies resultiert in neuen Anforderungen, die erfüllt
werden müssen, um sichere und zuverlässige autonome Fahrzeuge zu realisieren, die
weder Fahrzeuginsassen noch Passanten gefährden. Radarsysteme gehören zu den
Schlüsselkomponenten unter der Vielzahl der verfügbaren Sensorsysteme, da sie im
Gegensatz zu visuellen Sensoren von widrigen Wetter- und Umgebungsbedingungen
kaum beeinträchtigt werden. Darüber hinaus liefern Radarsysteme zusätzliche
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 für die Radarperzeption. Der andere Aspekt ist
die Beschleunigung der Validierung und Verifizierung von automated cyber-physical
systems, die parallel zum Automatisierungsgrad auch eine höhere Komplexität
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 gegenüber Rauschen übertrifft. 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 für die Signalerfassung um 70% reduziert
werden, während gleichzeitig die Robustheit der Radarwahrnehmung auch für 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 während der Simulation angewendet, um die Radarleistung
unter widrigen Bedingungen zu bewerten. Während eine Besprühung mit Wasser die
Radomdämpfung um 10 dB erhöht und Spritzwasser sogar um 20 dB, ergibt sich die
eigentliche Begrenzung aus der Rauschzahl und Empfindlichkeit des Empfängers. Es
konnte bewiesen werden, dass das vorgeschlagene Compressive Sensing Rauschradarsystem
mit einer zusätzlichen Signaldämpfung von bis zu 60 dB umgehen kann
und damit eine hohe Robustheit in ungünstigen Umwelt- und Wetterbedingungen
aufweist.
Neben der Robustheit wird auch die Interferenz berücksichtigt. 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 Einführung der Grundlagen
und Methoden für Fahrzeugradarsysteme werden die Theorie und Metriken hinter
Compressive Sensing gezeigt. Darüber hinaus werden weitere Aspekte wie Umgebungsbedingungen,
unterschiedliche Radararchitekturen und Interferenz erläutert.
Der Stand der Technik gibt einen Überblick über Compressive Sensing-Ansätze und
Implementierungen mit einem Fokus auf Radar. Darüber hinaus werden Aspekte
von Fahrzeug- und Rauschradarsystemen behandelt. Der Hauptteil beginnt mit
der Vorstellung verschiedener Ansätze zur Nutzung von Compressive Sensing für
Fahrzeugradarsysteme, die in der Lage sind, die Erfassung und Wahrnehmung von
Radarsignalen zu verbessern oder zu erweitern. Anschließend 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 schließt 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
Joint waveform and guidance control optimization by statistical linearisation for target rendezvous
The algorithm proposed in this paper jointly selects the transmitted waveform and the control input so that a radar sensor on a moving platform can prosecute a target by minimising a predefined cost that accounts for the energy of the transmitted radar signal, the energy of a platform control input and the relative position error between the platform and the target. The cost is a function of the waveform design and control input. The algorithm extends the existing Joint Waveform Guidance and Control Optimization (JWGCO) solution to nonlinear equations to account for the dependency of the radar measurement accuracies on Signal to Noise Ratio (SNR) and, as a consequence, on the target position. The performance of the proposed solution based on statistical linearisation is assessed with a set of simulations for a pulsed Doppler radar transmitting linearly frequency modulated chirps
Nearly orthogonal, doppler tolerant waveforms and signal processing for multi-mode radar applications
In this research, we investigate the design and analysis of nearly orthogonal, Doppler tolerant waveforms for diversity waveform radar applications. We then present a signal processing framework for joint synthetic aperture radar (SAR) and ground moving target indication (GMTI) processing that is built upon our proposed waveforms. ^ To design nearly orthogonal and Doppler tolerant waveforms, we applied direct sequence spread spectrum (DSSS) coding techniques to linear frequency modulated (LFM) signals. The resulting transmitted waveforms are rendered orthogonal using a unique spread spectrum code. At the receiver, the echo signal can be decoded using its spreading code. In this manner, transmit orthogonal waveforms can be matched filtered only with the intended receive signals. ^ Our proposed waveforms enable efficient SAR and GMTI processing concurrently without reconfiguring a radar system. Usually, SAR processing requires transmit waveforms with a low pulse repetition frequency (PRF) rate to reduce range ambigu- ity; on the other hand, GMTI processing requires a high PRF rate to avoid Doppler aliasing and ambiguity. These competing requirements can be tackled by employing some waveforms (with low PRF) for the SAR mission and other waveforms (with high PRF) for the GMTI mission. Since the proposed waveforms allow separation of individual waveforms at the receiver, we can accomplish both SAR and GMTI processing jointl