Orthogonal frequency division multiplexing multiple-input multiple-output automotive radar with novel signal processing algorithms

Advanced driver assistance systems that actively assist the driver based on environment perception achieved significant advances in recent years. Along with this development, autonomous driving became a major research topic that aims ultimately at development of fully automated, driverless vehicles. Since such applications rely on environment perception, their ever increasing sophistication imposes growing demands on environmental sensors. Specifically, the need for reliable environment sensing necessitates the development of more sophisticated, high-performance radar sensors. A further vital challenge in terms of increased radar interference arises with the growing market penetration of the vehicular radar technology. To address these challenges, in many respects novel approaches and radar concepts are required. As the modulation is one of the key factors determining the radar performance, the research of new modulation schemes for automotive radar becomes essential. A topic that emerged in the last years is the radar operating with digitally generated waveforms based on orthogonal frequency division multiplexing (OFDM). Initially, the use of OFDM for radar was motivated by the combination of radar with communication via modulation of the radar waveform with communication data. Some subsequent works studied the use of OFDM as a modulation scheme in many different radar applications - from adaptive radar processing to synthetic aperture radar. This suggests that the flexibility provided by OFDM based digital generation of radar waveforms can potentially enable novel radar concepts that are well suited for future automotive radar systems. This thesis aims to explore the perspectives of OFDM as a modulation scheme for high-performance, robust and adaptive automotive radar. To this end, novel signal processing algorithms and OFDM based radar concepts are introduced in this work. The main focus of the thesis is on high-end automotive radar applications, while the applicability for real time implementation is of primary concern. The first part of this thesis focuses on signal processing algorithms for distance-velocity estimation. As a foundation for the algorithms presented in this thesis, a novel and rigorous signal model for OFDM radar is introduced. Based on this signal model, the limitations of the state-of-the-art OFDM radar signal processing are pointed out. To overcome these limitations, we propose two novel signal processing algorithms that build upon the conventional processing and extend it by more sophisticated modeling of the radar signal. The first method named all-cell Doppler compensation (ACDC) overcomes the Doppler sensitivity problem of OFDM radar. The core idea of this algorithm is the scenario-independent correction of Doppler shifts for the entire measurement signal. Since Doppler effect is a major concern for OFDM radar and influences the radar parametrization, its complete compensation opens new perspectives for OFDM radar. It not only achieves an improved, Doppler-independent performance, it also enables more favorable system parametrization. The second distance-velocity estimation algorithm introduced in this thesis addresses the issue of range and Doppler frequency migration due to the target’s motion during the measurement. For the conventional radar signal processing, these migration effects set an upper limit on the simultaneously achievable distance and velocity resolution. The proposed method named all-cell migration compensation (ACMC) extends the underlying OFDM radar signal model to account for the target motion. As a result, the effect of migration is compensated implicitly for the entire radar measurement, which leads to an improved distance and velocity resolution. Simulations show the effectiveness of the proposed algorithms in overcoming the two major limitations of the conventional OFDM radar signal processing. As multiple-input multiple-output (MIMO) radar is a well-established technology for improving the direction-of-arrival (DOA) estimation, the second part of this work studies the multiplexing methods for OFDM radar that enable simultaneous use of multiple transmit antennas for MIMO radar processing. After discussing the drawbacks of known multiplexing methods, we introduce two advanced multiplexing schemes for OFDM-MIMO radar based on non-equidistant interleaving of OFDM subcarriers. These multiplexing approaches exploit the multicarrier structure of OFDM for generation of orthogonal waveforms that enable a simultaneous operation of multiple MIMO channels occupying the same bandwidth. The primary advantage of these methods is that despite multiplexing they maintain all original radar parameters (resolution and unambiguous range in distance and velocity) for each individual MIMO channel. To obtain favorable interleaving patterns with low sidelobes, we propose an optimization approach based on genetic algorithms. Furthermore, to overcome the drawback of increased sidelobes due to subcarrier interleaving, we study the applicability of sparse processing methods for the distance-velocity estimation from measurements of non-equidistantly interleaved OFDM-MIMO radar. We introduce a novel sparsity based frequency estimation algorithm designed for this purpose. The third topic addressed in this work is the robustness of OFDM radar to interference from other radar sensors. In this part of the work we study the interference robustness of OFDM radar and propose novel interference mitigation techniques. The first interference suppression algorithm we introduce exploits the robustness of OFDM to narrowband interference by dropping subcarriers strongly corrupted by interference from evaluation. To avoid increase of sidelobes due to missing subcarriers, their values are reconstructed from the neighboring ones based on linear prediction methods. As a further measure for increasing the interference robustness in a more universal manner, we propose the extension of OFDM radar with cognitive features. We introduce the general concept of cognitive radar that is capable of adapting to the current spectral situation for avoiding interference. Our work focuses mainly on waveform adaptation techniques; we propose adaptation methods that allow dynamic interference avoidance without affecting adversely the estimation performance. The final part of this work focuses on prototypical implementation of OFDM-MIMO radar. With the constructed prototype, the feasibility of OFDM for high-performance radar applications is demonstrated. Furthermore, based on this radar prototype the algorithms presented in this thesis are validated experimentally. The measurements confirm the applicability of the proposed algorithms and concepts for real world automotive radar implementations

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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Integrated Communication and Radar Scheme for Future Intelligent Transportation Systems

RÉSUMÉ Grâce à son impact social et économique, la journée mondiale de la santé 2004 a été dédiée à la sécurité routière. Le thème suivant : « La sécurité routière n‘est pas accidentelle» a été abordé. Suite à cette rencontre, une attention toute particulière a été donnée à la problématique des accidents de la route. Afin d‘augmenter la sécurité sur les routes et diminuer le nombre d‘accidents, des systèmes intelligents de transport (ITS) ont été proposés. Ces systèmes utilisent les technologies avancées de communication et de détection. La structure ITS associe les fonctionnalités des Radars et des communications sans fils, permettant de rendre les futurs véhicules intelligents autonomes et collaboratifs. Ces deux fonctions peuvent être réalisées en utilisant deux systèmes radiofréquences individuels et indépendants. Toutefois, une meilleure solution consiste à intégrer, dans un seul dispositif, le système de communication et le radar. Ceci permet d‘apporter de nombreux avantages comme par exemple la simplification et la miniaturisation du système, sa reconfigurabilité, l‘augmentation de son efficacité, et enfin cela permettrait de réduire fortement ses coûts de développement et de réalisation, élément clé pour réussir la commercialisation du véhicule intelligent. Intrinsèquement, le fonctionnement des communications sans fils et des Radar ne sont pas compatibles. En effet, ils requièrent des techniques de conception et d‘implémentations différentes, ce qui les rend difficilement intégrables en un seul système. Afin de répondre aux grands défis technologiques présentés par cette intégration fonctionnelle, cette thèse de doctorat présente un développement compréhensif des systèmes intégrés de communication sans-fil et radar (iCars), placés dans un seul dispositif émetteur-récepteur et destinés aux futurs systèmes intelligents de transport. Premièrement, après une recherche bibliographique approfondie, une nouvelle technique de modulation est proposée. Dans cette technique, les signaux radar et les signaux de communication sont arrangés en créneaux temporels séquentiels pendant un cycle d‘opération, minimisant ainsi leurs interférences mutuelles. Cette technique permet d‘obtenir une agilité temporelle et/ou une reconfigurabilité fonctionnelle, par l‘ajustement adaptatif ou cognitif de toutes les durées de modulation de la forme d‘onde, en accord avec les situations spécifiques de l‘utilisation.----------ABSTRACT Due to its growing social and economic impact, the world health day of 2004 was dedicated to road safety with its theme as ―Road safety is no accident‖. Thereafter, road traffic accidents have received unprecedented attention. In order to improve road safety, intelligent transportation systems (ITSs) have been proposed and deployed by making use of advanced information and communication technologies. Within the framework of ITSs, both wireless communication and radar sensing functions are indispensable for autonomous and cooperative operations of future intelligent vehicles (IVs). These two functions can definitely be achieved by using two individual and independent wireless systems. However, an attractive solution would be to integrate both communication and radar functions within a single transceiver platform, which could bring a lot of benefits such as system simplification and miniaturization, functional reconfiguration and fusion (mutual penetration and rapid processing/control of information), and especially efficiency enhancement and cost reduction that are the keys to the successful development and marketing of IVs. Intrinsically, wireless communication and radar systems have incompatible operation principles, which require different design considerations and system implementations with respect to modulation techniques, required bandwidth, signal propagation and detection. To respond to these unprecedented design and technological challenges posed by the functional integration, this PhD thesis presents comprehensive study and development of integrated communication and radar systems (iCars) based on a single transceiver platform for future ITSs. Following a broad and in-depth literature review, first of all, a novel modulation scheme is proposed in this work, in which radar and communication signals are arranged in sequential time slots of one operation cycle and therefore, their interference is minimized. Also, time-agility or flexible functional reconfiguration can be easily achieved by adaptively or cognitively adjusting all software-programmable time durations in the modulation waveform according to usage situations. Moreover, functional fusion between two operation modes can be made possible from the following two aspects. One is that targets‘ ranges and velocities obtained through the radar mode can be used in the communication mode to mitigate multipath fading and compensate the Doppler spreading effect caused by the mobility of onboard units

Short-Range Millimeter-Wave Sensing and Imaging: Theory, Experiments and Super-Resolution Algorithms

Recent advancements in silicon technology offer the possibility of realizing low-cost and highly integrated radar sensor and imaging systems in mm-wave (between 30 and 300 GHz) and beyond. Such active short-range mm-wave systems have a wide range of applications including medical imaging, security scanning, autonomous vehicle navigation, and human gesture recognition. Moving to higher frequencies provides us with the spectral and spatial degrees of freedom that we need for high resolution imaging and sensing application. Increased bandwidth availability enhances range resolution by increasing the degrees of freedom in the time-frequency domain. Cross-range resolution is enhanced by the increase in the number of spatial degrees of freedom for a constrained form factor. The focus of this thesis is to explore system design and algorithmic development to utilize the available degrees of freedom in mm-wave frequencies in order to realize imaging and sensing capabilities under cost, complexity and form factor constraints. We first consider the fundamental problem of estimating frequencies and gains in a noisy mixture of sinusoids. This problem is ubiquitous in radar sensing applications, including target range and velocity estimation using standard radar waveforms (e.g., chirp or stepped frequency continuous wave), and direction of arrival estimation using an array of antenna elements. We have developed a fast and robust iterative algorithm for super-resolving the frequencies and gains, and have demonstrated near-optimal performance in terms of frequency estimation accuracy by benchmarking against the Cramer Rao Bound in various scenarios.Next, we explore cross-range radar imaging using an array of antenna elements under severe cost, complexity and form factor constraints. We show that we must account for such constraints in a manner that is quite different from that of conventional radar, and introduce new models and algorithms validated by experimental results. In order to relax the synchronization requirements across multiple transceiver elements we have considered the monostatic architecture in which only the co-located elements are synchronized. We investigate the impact of sparse spatial sampling by reducing the number of array antenna elements, and show that ``sparse monostatic'' architecture leads to grating lobe artifact, which introduces ambiguity in the detection/estimation of point targets in the scene. At short ranges, however, targets are ``low-pass'' and contain extended features (consisting of a continuum of points), and are not well-modeled by a small number of point scatterers. We introduce the concept of ``spatial aggregation,'' which provides the flexibility of constructing a dictionary in which each atom corresponds to a collection of point scatterers, and demonstrate its effectiveness in suppressing the grating lobes and preserving the information in the scene.Finally, we take a more fundamental and systematic approach based on singular decomposition of the imaging system, to understand the information capacity and the limits of performance for various geometries. In general, a scene can be described by an infinite number of independent parameters. However, the number of independent parameters that can be measured through an imaging system (also known as the degrees of freedom of the system) is typically finite, and is constrained by the geometry and wavelength. We introduce a measure to predict the number of spatial degrees of freedom of 1D imaging systems for both monostatic and multistatic array architectures. Our analysis reveals that there is no fundamental benefit in multistatic architecture compared to monostatic in terms of achievable degrees of freedom. The real benefit of multistatic architecture from a practical point of view, is in being able to design sparse transmit and receive antenna arrays that are capable of achieving the available degrees of freedom. Moreover, our analytical framework opens up new avenues to investigate image formation techniques that aim to reconstruct the reflectivity function of the scene by solving an inverse scattering problem, and provides crucial insights on the achievable resolution

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges

Microwave resonant sensors

Microwave resonant sensors use the spectral characterisation of a resonator to make high sensitivity measurements of material electromagnetic properties at GHz frequencies. They have been applied to a wide range of industrial and scientific measurements, and used to study a diversity of physical phenomena. Recently, a number of challenging dynamic applications have been developed that require very high speed and high performance, such as kinetic inductance detectors and scanning microwave microscopes. Others, such as sensors for miniaturised fluidic systems and non-invasive blood glucose sensors, also require low system cost and small footprint. This thesis investigates new and improved techniques for implementing microwave resonant sensor systems, aiming to enhance their suitability for such demanding tasks. This was achieved through several original contributions: new insights into coupling, dynamics, and statistical properties of sensors; a hardware implementation of a realtime multitone readout system; and the development of efficient signal processing algorithms for the extraction of sensor measurements from resonator response data. The performance of this improved sensor system was verified through a number of novel measurements, achieving a higher sampling rate than the best available technology yet with equivalent accuracy and precision. At the same time, these experiments revealed unforeseen applications in liquid metrology and precision microwave heating of miniature flow systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Microwave resonant sensors

Microwave resonant sensors use the spectral characterisation of a resonator to make high sensitivity measurements of material electromagnetic properties at GHz frequencies. They have been applied to a wide range of industrial and scientific measurements, and used to study a diversity of physical phenomena. Recently, a number of challenging dynamic applications have been developed that require very high speed and high performance, such as kinetic inductance detectors and scanning microwave microscopes. Others, such as sensors for miniaturised fluidic systems and non-invasive blood glucose sensors, also require low system cost and small footprint. This thesis investigates new and improved techniques for implementing microwave resonant sensor systems, aiming to enhance their suitability for such demanding tasks. This was achieved through several original contributions: new insights into coupling, dynamics, and statistical properties of sensors; a hardware implementation of a realtime multitone readout system; and the development of efficient signal processing algorithms for the extraction of sensor measurements from resonator response data. The performance of this improved sensor system was verified through a number of novel measurements, achieving a higher sampling rate than the best available technology yet with equivalent accuracy and precision. At the same time, these experiments revealed unforeseen applications in liquid metrology and precision microwave heating of miniature flow systems