The increasing accessibility of consumer drones raises safety and security risks, emphasising the necessity of reliable drone detection systems. This thesis characterises the radar cross-section of two commercial drones at 24\,GHz frequency: the DJI Air 3S and DJI Neo, forming part of a broader drone detection and tracking system using frequency-modulated continuous wave radar. Measurements were performed in the Delft University Chamber for Antenna Tests anechoic chamber across a range of azimuth and elevation angles, using two different-bandwidth radar configurations. Here background subtraction and range-gating was utilised to process the results. The higher bandwidth dataset yielded an improved transmit-receive isolation and was prioritised for statistical modelling. Elevation angle configurations were pooled into a combined dataset, due to being considered a non-significant factor in forming the radar cross-section distribution. Five candidate models were compared, including the Swerling I and III target models, the gamma, Weibull, and log-normal distributions. Model selection was based on goodness-of-fit tests including Kolmogorov-Smirnov and Pearson chi-squared tests, along with the Akaike and Bayesian information criteria. Swerling III was deemed the optimal model for the DJI Air 3S, due to statistically performing the best and also being physically grounded; Swerling III captures a dominant scatterer with smaller adjacent scatterers, where the DJI Air 3S has a main body and smaller, protruding propeller arms. For the DJI Neo, the Weibull distribution was selected, while being statistically tied with Swerling III, as its shape parameter provides a measure for radar cross-section fluctuation behaviour, and the drone's compact geometry is insufficiently consistent with the physical implications of Swerling III. ... Read more

Bounded-wave NEMP simulators cannot accommodate a full-scale warship and the conventional radiating alternative, a large monocone, cannot deliver sufficient field strength at acceptable homogeneity over the testing surface. This thesis investigates an unconventional architecture for radiated NEMP hardness validation of large naval platforms: a timed array of directive, ultra-wideband radiators that deliberately trades full-platform illumination for local higher field strength and better homogeneity.

Starting from the NEMP standards, a set of field-strength, homogeneity and timing requirements is distilled, which may be interpreted as design specifications. The monocone baseline (EMIS-III-VPD) is characterised and shown to meet none of the three criteria, achieving only 1.28~kV/m peak field, roughly 10~dB amplitude inhomogeneity and 138~ns of peak-time spread across a testing surface of 200 m x 50 m at 100 m distance. \\

The design process is started by evaluating candidate antenna concepts that are all at least ultra-wideband and non-dispersive. It is revealed that directivity is the enabling factor for antennas in radiated NEMP testing, resulting in the reflector IRA to be selected as element in the array. Subsequently, this antenna is modelled at three scales in a full wave-solver, exposing the trade-off between aperture size and low-frequency operating limit. Using results from the full-wave solver, the array is synthesized and optimized in MATLAB using a multi-objective genetic algorithm. This was done over a far-field model anchored to the outcome from the full-wave solver, reducing runtime significantly. With peak field strength imposed as a minimum, the amplitude and timing homogeneity could be minimised simultaneously.

A single 12.2~m IRA fed at 500~kV radiates 4.5~kV/m. An optimised four-element array reaches 9.6~kV/m peak at 0.56~dB inhomogeneity and 1.06~ns peak-time spread over a 10$\times$10~m surface at 100 m distance. The array is therefore a peak-field multiplier rather than a means of improving homogeneity: the single antenna is the most uniform configuration of any studied, and optimisation only recovers near-single-antenna uniformity up to a ceiling of roughly 9~kV/m, beyond which it degrades steeply. The 50~kV/m of the standards is unreachable by this approach, since the feasible element counts and feed voltages required are impractical and the binding limits, aperture size and voltage breakdown, lie outside the array. This contribution is not a finished simulator but a quantified baseline, and bounds on what a radiated NEMP test of a large platform can realistically achieve, as well as a framework within which future work may proceed from. ... Read more

The use of lasers for satellite communication has recently become a growing field. However, a major challenge in their use is the distortion caused by the atmosphere. The distortion that this study focuses on is optical turbulence. The strength of optical turbulence is related to fluctuations in the refractive index of air and is generally described by Cn². There is a growing body of literature that aims to measure this Cn². One of the methods uses radar, and works by receiving backscattered echoes from turbulent eddies under clear-sky conditions, also known as Bragg scattering. This thesis investigates whether this method, that has so far only been applied to pulsed Doppler weather radars, can also be used with an S-band Frequency-Modulated Continuous-Wave (FMCW) radar.

In this work numerical radar data were used to filter out noise and isolate Bragg scattering. When only clear-sky days in June were included, the results from the 12 selected days showed an average correlation of 0.48 when compared with scintillometer validation data. This is higher than the correlation coefficient of 0.34 found using the pulse-Doppler radar method. However the results also showed a large systematic error, which was found to be related to calibration issues of the used radar. Because this error remained relatively stable over time, a correction factor could potentially be applied to compensate for it. Overall, the results indicate that it is possible to measure optical turbulence using vertical reflectivity measurements from a FMCW radar, although further research and improvements are needed before the method can be used operationally. ... Read more

In this thesis, the design and implementation of a target tracking subsystem is outlined as part of a radar-based drone detection system. The end-goal of the tracking subsystem is to provide actionable data to the end-user, allowing appropriate action to be taken within the target application. Radar detections are simulated with their associated noise, uncertainty, and clutter. Field measurements show that the tracker is able to initiate tracks and follow the drone trajectory despite the added challenges of sensor measurement noise, missed detections, and false detections.
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Autonomous driving requires reliable perception under diverse and adverse conditions. Among modern sensing modalities, automotive radar plays a unique role due to its robustness in poor weather and low visual visibility, its direct measurement of radial velocity via the Doppler effect, and its ability to detect objects beyond the line of sight. Despite these advantages, radar data are sparse, noisy, and affected by artifacts such as multipath reflections and sidelobes, resulting in weak geometric representations and high false alarm rates. Consequently, perception methods developed for cameras and lidar cannot be directly transferred to radar. This dissertation advances robust radar perception for autonomous vehicles by developing deep learning methods tailored to radar characteristics for motion estimation, multi-radar fusion, extrinsic calibration, and scene segmentation. The central research question is whether radar can be elevated from a supporting sensor to a primary perception modality capable of delivering robust and accurate information for automotive applications. To address this, the thesis investigates which tasks are best suited to radar, how deep learning can be effectively integrated with radar data, how multiple radars can be fused without strict synchronization, how extrinsic misalignment can be corrected during normal driving, and whether common perception requirements can be addressed within a unified framework. ... Read more

Quantum bits (qubits) are regarded as the building blocks of a quantum computer, which have significant implications in quantum information science. The superconducting qubit is a solid platform for quantum computing, where the Transmon qubit based on the Josephson junction or the SQUID structure is widely designed and researched. Microwave design and electromagnetic simulation of a superconducting qubit is a crucial way to determine and enhance the qubit performance in reality. The geometry of a superconducting qubit structure should be optimized to achieve the desired equivalent capacitance and consequently the resonant frequency through the external magnetic flux adjustment. In this thesis, a special superconducting qubit for lower energy loss named 'Pokemon' qubit is analyzed, with the role of different capacitance geometry in the qubit studied. This thesis also presented a comparative analysis between the in-plane capacitive structure and the flip-chip capacitance. The results pave the way for future modular quantum processing units utilizing flip-chip technology. ... Read more

This thesis investigates high-resolution direction-of-arrival (DOA) estimation in a distributed automotive MIMO radar system consisting of two radars and four bistatic/monostatic apertures. To achieve super-resolution while keeping the computational load feasible, a two-stage DOA framework is developed: coarse beamforming with multi-aperture data association, followed by fine sparse reconstruction using both non-coherent and coherent FOCUSS. A complete phase-synchronization and geometric compensation model is derived to enable coherent fusion, and several numerical-stabilization techniques are introduced to ensure reliable reconstruction. Finally, this work proposes a modified array configuration that suppresses sidelobes for large baselines, significantly improving coherent performance. The results demonstrate enhanced angular resolution, robustness, and the feasibility of coherent sensing in distributed automotive radar. ... Read more

The rapid advancement of next-generation wireless communication and sensing technologies demands high-performance, intelligent antenna systems capable of adaptive beamforming, efficient signal control, and reliable operation under dynamic conditions. Active phased arrays, especially non-uniform (aperiodic) layouts, offer flexibility for power-efficient side-lobe control and improved spatial coverage, but at the cost of increased electromagnetic, thermal, and computational complexity. Practical IC-integrated aperiodic arrays therefore require innovative design and diagnostic frameworks that efficiently address challenges across pre- and post-manufacturing stages.

Traditionally, design and performance evaluation of active phased arrays have relied on deterministic electromagnetic simulations or optimization-based methods focusing on radiation characteristics such as gain, beamwidth, and side-lobe levels (SLL). As arrays grow more complex, such methods face limitations in scalability and computational feasibility. Machine learning (ML) offers a pathway to overcome these barriers by enabling data-driven modeling, optimization, and diagnosis of large-scale or non-uniform active arrays. This interdisciplinary challenge lies at the intersection of electromagnetics (EM), array modeling, and data-driven methods, requiring frameworks that balance physical accuracy with computational efficiency.

The objective of this doctoral research is to develop and validate ML-based methodologies that enhance both the design and operation of active arrays, emphasizing efficient modeling, synthesis, and diagnosis under mutual coupling (MC), environmental effects, and fabrication-induced uncertainties. The research addresses both pre-manufacturing (e.g., synthesis, EM modeling, and topology optimization) and post-manufacturing (e.g., calibration and fault detection) aspects of active arrays, with a focus on data-efficient and physics-supported ML techniques.

Chapter II introduces the motivation, state-of-the-art challenges in phased array systems, and the potential of ML in overcoming these limitations. A detailed analysis highlights critical issues of mutual coupling, irregular routing, calibration complexity, and environmental sensitivity, establishing the need for intelligent, adaptive frameworks that integrate ML with physical modeling to enhance design reliability and post-deployment robustness.

Chapter III presents data-driven modeling of EM interactions in aperiodic phased arrays. A neural network (NN)-based framework predicts embedded element patterns (EEPs) across the visible space for non-uniform planar arrays. The cascaded NN architecture, combining a fully connected NN and a sub-pixel convolutional network, enables high-resolution pattern prediction with significant computational savings. The influence of dataset size and quality on prediction reliability is analyzed, revealing insights into data efficiency and model generalization.

Chapter IV introduces hybrid ML-physics approaches to improve prediction robustness. Two basis-function-assisted frameworks are developed using the Infinitesimal Dipole Model (IDM) and spherical harmonics. These models achieve compact, reliable EEP representation with smaller datasets while mitigating numerical instability. An ensemble method combining NN-based EEP prediction and constrained IDM achieves a 60% reduction in mean squared error (MSE) and improved prediction stability under MC effects.

Chapter V applies these ML-assisted models to system-level synthesis and diagnostics. A novel ML-driven optimization method for MIMO radar arrays integrates spherical harmonics-based EEP prediction into a particle swarm optimization (PSO) routine, enabling efficient MC-aware array topology design and minimizing maximum SLL in multi-beam configurations. ML-based post-manufacturing diagnostics are demonstrated for a 64-element active uniform array, where a fully connected NN detects faulty elements in real time using sparse far-field amplitude data.

The findings demonstrate that ML can bridge the gap between EM theory and practical array implementation. The developed frameworks offer data-efficient alternatives for MC-aware active array optimization and system diagnostics, paving the way toward intelligent, reliable, and self-adaptive active array systems for future wireless communication and sensing applications.
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Modern, multifunctional ground-based weather radar systems deployed at the airports are designed to detect and track point-like targets, such as birds and drones. Therefore, these radars scan the field of view in azimuth very fast to get quick updates on the whereabouts of these targets. The fast scanning nature of these radars limits their capacity to accurately estimate parameters for weather applications, such as the precipitation intensity and velocity distribution parameters of the raindrops during rainy conditions. This thesis aims to develop novel techniques to estimate the parameters related to the precipitation Doppler spectrum with limited time on target. In addition to estimating the parameters, this thesis extensively discusses the trade-offs and recommends
application-specific measurement techniques.... ... Read more

Radar sensors are an emerging technology in the context of non-contact monitoring of vulnerable individuals. Radar-based solutions ensure end-user privacy, whilst providing medical professionals and caregivers with key information concerning the subject's well-being. This thesis proposes novel methods for the classification of sequential human activities using a network of radar sensors. Accurate classification of Activities of Daily Life (ADL) can enable for instance the detection of falls and wandering amongst elderly individuals, and can be employed for the recognition of aggressive or otherwise anomalous behaviour for those receiving mental health care. ... Read more

Today, state-of-the-art device manufacturing for high-frequency (HF) electromagnetic (EM) applications is primarily dominated by subtractive techniques, including milling, laser cutting, electric discharge machining, and chemical etching, among others. While these methods are well established and enable remarkable accuracy, they are fundamentally constrained in terms of the geometric complexity they can achieve. The growing demand for more compact, efficient, and high-bandwidth EM devices challenges these limitations. Consequently, there has been a surge in alternative manufacturing techniques that leverage three-dimensional space to unlock additional design freedoms. The present work explores the unique capabilities of extrusion-based additive manufacturing (AM) to realize complex periodic dielectric structures for modern high-frequency EM applications. Specifically, this thesis investigates the homogenization, characterization, and practical implementation of periodic dielectric structures—also referred to as dielectric crystals—for advanced EM applications at microwave and millimeter-wave frequencies.
The thesis opens with a comprehensive review of AM applications in HF electromagnetics, highlighting significant potential while also identifying critical gaps in the current state of the art. These gaps guide the original contributions of the presented research, summarized below.

First, a comprehensive overview and comparison of homogenization and experimental characterization techniques for evaluating dielectric material properties is provided. While literature offers treatments on numerous analytical, numerical, and experimental methods, there is a lack of a systematic comparison tailored specifically to AM periodic dielectric structures. This thesis bridges that gap by rigorously comparing selected homogenization methods against experimental characterization results. It provides an analysis of the advantages and limitations of each approach and identifies their suitability for analysing AM periodic dielectrics.

Second, the impact of additive fabrication on the anisotropy of dielectric material properties is studied. The phenomenon of mechanical anisotropy resulting from the layer-by-layer additive fabrication approach is well-documented in literature, while its implications for dielectric properties remain unexplored. The presented research addresses this critical gap by thoroughly investigating the effective permittivity tensor. For this purpose, an analytical model is presented and its predictions validated through numerical simulations and experimental characterization using various materials and printing parameters. The results confirm significant anisotropy with negative birefringence, aligning well with theoretical predictions.

Third, although previous studies have utilized volumetric infill variations to influence the effective dielectric properties of periodic structures, the fundamental connection between crystallographic theory and dielectric behavior has remained largely unexplored. This thesis systematically investigates how lattice symmetries affect the effective permittivity tensor. A detailed parametric study of selected Bravais lattices and crystal centerings is conducted, demonstrating precise control over dielectric anisotropy. These findings are confirmed experimentally through careful characterization of several lattice structures exhibiting distinct uniaxial and biaxial anisotropic properties.

Fourth, spatially modulated dielectric structures have mostly been employed in graded-index (GRIN) lenses for long-wavelength applications. This work expands significantly beyond this scope, exploring new applications in both the long- and short-wavelength operating regimes. For long-wavelength scenarios, cylindrical heterogeneous dielectric resonator antennas (DRAs) with radially and vertically varying permittivity profiles are investigated. Experimental results demonstrate that spatial modulation enhances both the impedance and axial ratio bandwidth compared to homogeneous DRAs. Furthermore, heterogeneous dielectric slabs within rectangular waveguides are explored, demonstrating practical control over transmission and reflection characteristics, exemplified through the realization of a third-order Chebyshev bandpass filter.
Regarding short-wavelength applications, this research explores 3D periodic dielectric bandgap materials. A thorough numerical and experimental verification of the bandgap properties of woodpile structures is conducted, culminating in a novel dielectric rod antenna that significantly suppresses third harmonic radiation through combined long- and short-wavelength concepts. Collectively, these examples demonstrate the vast potential for innovative device performance enabled by spatially modulated periodic dielectric structures.

Fifth, integrating conductive and dielectric materials in a single AM process is highly advantageous yet underutilized, primarily due to the prohibitive cost of specialized equipment. Addressing this limitation, the thesis introduces an accessible, open-source inspired approach to hybrid AM. A custom-built AM system incorporating a micro-dispensing head for conductive ink deposition is successfully demonstrated through the fabrication of a fully additive-manufactured dielectric resonator phased array antenna operating at $20$\si{\giga\hertz}. This practical demonstration confirms the viability and effectiveness of hybrid AM technology for advanced antenna applications.

In summary, this research underscores the transformative potential and enables new opportunities of additive manufacturing in EM device engineering. While recognizing the ongoing challenges related to material and equipment limitations, the thesis illustrates that these can be effectively managed through careful selection of homogenization, characterization, and design methodologies. Promising future directions identified include further exploration of hybrid AM for high-frequency EM applications, innovations in communications, radar, and sensing technologies, and continued advancements in integrated material processes. Ultimately, this work significantly advances additive manufacturing for electromagnetic applications, laying a strong foundation for future innovations in communication, radar, and sensing technologies. ... Read more

This dissertation pushes orthogonal time‑frequency space (OTFS) from a communications-centric innovation to a radar‑ready waveform family suitable for future 6G integrated sensing and communications (ISAC). It is driven by the need to retain OTFS’s Doppler resilience while overcoming three main drawbacks: a lack of a fair comparison study with orthogonal frequency‑division multiplexing (OFDM) for radar tasks, (ii) its rigid, fully populated time‑frequency (TF) structure that hinders multiple‑access flexibility, and (iii) its high peak‑to‑average‑power ratio (PAPR), which obstructs high‑power operation, crucial for many sensing applications. The dissertation delivers a coherent toolkit that clarifies the true origin of OTFS sensing gains, provides the waveform with OFDM‑like scheduling freedom via the novel and well-researched NU‑OTFS, and offers an alternative to remove the PAPR bottleneck through CM‑OTFS. Collectively, these contributions support OTFS in becoming a practically deployable candidate for next‑generation ISAC networks that demand dense MIMO operation, fragmented spectral access, and high‑power front‑ends. ... Read more

Active phased array antennas, generally consisting of a large number of antenna elements and integrated-circuit (IC) components, are essential for different applications like 5G/6G communications, radar, aerospace, satellite communications and so on. The study of radiation pattern measurements and analysis of pattern data is a pivotal step in characterizing and understanding the use of antennas in any application. Unwanted changes can be observed in the radiation pattern due to randomly failed elements, ICs, or IC channels in phased arrays. Hence, there is a vital need for fast testing and diagnosis to identify possible failures of these large antenna systems, before taking any immediate action to compensate for these changes. Due to integrated-circuits (IC) non-linearities and mutual coupling challenges, the existing deterministic and optimization-based fault detection techniques fail to provide reliable results in practical systems. Besides, it is desired to achieve real-time array diagnosis during in-field operation, which makes the problem even more challenging. In this thesis, a novel machine-learning assisted fault-detection algorithm is proposed which is trained by using sparse far-field measurements from a practical IC-integrated millimeter-wave (mm-wave) phased array prototype and fixed multi-probe measurement system (antenna dome). A global optimization approach is used as a benchmark for comparison to the state-of-the-art. The incorporation of machine learning techniques greatly improves the fault detection time and accuracy in comparison to global optimizers. Furthermore, studies based on the number of sampling points used and the varying number of faults are also conducted which indicate similar conclusions. ... Read more

Autonomous driving is one of the most popular research topics. Radar technology is used for many applications of ADAS and is considered one of the key technologies for HAD. It has unique advantages compared with other sensors, especially its capabilities during adverse weather conditions and Doppler information extraction. Required by autonomous applications, radar has to change its historical role from a simple detector to an imaging sensor, which requires not only the range and Doppler resolution ability but also a high spatial resolution, i.e., the azimuth and the elevation angle resolution. To address this problem, in this thesis, new signal processing algorithms are proposed, which pave the way to improved performance of the automotive radar sensor.
The FMCW waveform is widely used in current automotive applications due to its low cost and simplicity. MIMO array techniques exploit the spatial diversity of transmit and receive antenna arrays and have been exploited in current automotive radar because of their ability to achieve high angular resolution with a few antennas. Platform movement is one of themain characteristics of automotive radar, which introduces movement uncertainty compared with radars at fixed locations but provides an opportunity to use the movement to boost the angular resolution. Thus, FMCW waveform and MIMO antenna array are the main research subjects in this thesis..... ... Read more

Due to recent advances in miniaturisation and cost reductions of radar systems, an increasing amount of vehicles is equipped with automotive radar systems to enable advanced driver assistance systems (ADAS) which improve driving comfort and road safety. Examples of these systems include automatic braking as well as adaptive cruise control. To advance the capabilities of these systems and to take another step towards enabling autonomous driving, the next generation of radar sensors aims to enhance the situational awareness of the driver and the vehicle by, amongst others, classifying objects in the surrounding area of the car. Arguably, the most important objects to correctly classify in this process are vulnerable road users (VRUs).

With the largely completed transition of automotive radar systems from the previously used 24 GHz band to the new 77 GHz band, the performance of automotive radar systems has improved further due to larger available bandwidths and reduced wavelengths. The reduced wavelengths allow for an increased angular resolution with the same aperture size while the increased bandwidth results in enhanced range resolution. However, all currently commercially available automotive radar systems are single-polarised and therefore do not consider an important source of information, namely the polarisation state of the backscattered radiation, which can benefit classification of objects greatly.

To gain better insight in the benefits of this factor, this thesis is concerned with investigating the effectiveness of polarisation information for classification of objects in the automotive scenario. In particular, two types of objects are considered, namely road surfaces and their conditions, as well as vulnerable road users.

Chapter 2 considers the scattering of electromagnetic waves from road surfaces. Measurements on multiple different road surface materials are performed and their electrical properties are determined. These measurement results are subsequently used to simulate scattering from road surfaces at mm-wave frequencies using numerical methods. Also, a new method for computing the range-Doppler signature of road surfaces in dynamic scenarios is proposed.

In chapter 3, to accompany the numerical results from the previous chapter, a method is developed to determine statistical polarimetric radar cross section models of road surfaces at mm-wave frequencies. Measurements are performed and the radar cross section models of several types of surfaces and road surface conditions are determined. These models are subsequently used to simulate polarimetric backscattering from road surfaces as well as to determine the optimal sensing wave polarisation to be used for single-polarised radar systems.

Chapter 4 considers the classification of road surfaces and road surface conditions using H, α, and A features. For the first time, these features are adapted for road surface classification purposes in the automotive scenario and it is shown that these features can benefit surface classification.

Chapter 5 shifts the attention from classification of road surface conditions to classification of vulnerable road users. In order to do this, a novel automotive polarimetric MIMO radar system with corresponding signal processing algorithms has been developed and is presented in this chapter. This radar is subsequently used for measurements of moving VRUs and several features are considered to classify them. It is demonstrated that polarimetric information is indeed able to increase classification perfomance of VRUs compared to single polarised radar.

This thesis demonstrates that polarimetric radar is useful in the automotive scenario besides the fields in which it has already been established such as synthetic aperture radar and meteorological radar applications. The methods proposed within this dissertation can be used to enhance classification accuracy of road surface condition which may lead to improvements in road safety by for example enhancing the performance of anti-lock breaking systems. Also, the methods proposed in this thesis to use polarimetric information for classifying vulnerable road users are shown to improve classification performance of this type of objects which could help enable autonomous driving in the future. ... Read more

This thesis focuses on advancing radar technology to meet the growing demands of autonomous driving, particularly regarding angular resolution and target detection. The work acknowledges the critical role of automotive radar in achieving higher levels of vehicle autonomy, where reliable detection, classification, and tracking of objects like pedestrians, vehicles, and infrastructure are critical. The primary focus of the dissertation is to explore novel techniques for enhancing radar system performance through machine learning (ML) and compressive sensing (CS) approaches, addressing the challenges faced by current radar technologies, such as low angular resolution and inefficient target detection in dynamic environments.

The dissertation begins by outlining the challenges in automotive radar systems, especially the need for improved angular resolution without increasing the physical size and complexity of the radar devices. The importance of angular resolution is emphasized for both azimuth and elevation, as modern vehicles must be able to discriminate between various objects, such as distinguishing between two vehicles at a similar distance, or identifying an object’s height to determine whether it can be driven under or must be avoided. Current methods for improving angular resolution, such as increasing the number of transmitters and receivers in multiple input multiple output (MIMO) radars, are costly and increase system complexity, thus requiring novel solutions to meet industry needs. Then, Chapter 2 briefly summarizes the theoretical background of MIMO radars and defines the terminology used in the rest of the dissertation. This is crucial since automotive radar is a multi-disciplinary topic with people from different backgrounds interacting, and often, the same concepts are named differently.

The first research chapter, Chapter 3, introduces a self-supervised learning framework designed to enhance the angular resolution of radar systems without the need for additional physical hardware. A neural network (NN) artificially expands the radar’s aperture by predicting the response of additional antenna elements based on data from radars with larger arrays. This approach leverages the correlation between antenna elements to generate a more detailed angular profile from a smaller, low-resolution radar, allowing for a more accurate estimation of incoming signals' direction of arrival (DoA). Extensive simulations and experimental results demonstrate that this method significantly enhances radar performance in separating closely spaced objects, which is critical in automotive scenarios.

Another key contribution of the dissertation is presented in Chapter 4, with the application of Bayesian compressive sensing (BCS) to automotive radar, which exploits the sparse nature of the data in the DoA domain. The BCS approach uses probabilistic models to estimate the DoA while also providing uncertainty measures, offering both accuracy and reliability in angular estimation. The research further explores how array topologies can be optimized for BCS-based DoA estimation, demonstrating that a carefully designed antenna array can achieve better performance with fewer elements, thus reducing system costs. Additionally, the work presents a computationally efficient BCS algorithm that dramatically reduces the time needed for DoA estimation without compromising accuracy. This is a crucial advancement for real-time applications, where fast processing is required for decision-making in autonomous driving.

In addition to BCS, in Chapter 5, total variation compressive sensing (TVCS) is applied to the problem of radar imaging. TVCS enforces sparsity in the gradient of the signal rather than in the signal itself, which proves particularly effective in estimating the shape of extended targets, such as vehicles or pedestrians. By applying TVCS to 2D and 3D radar data, the dissertation demonstrates that this method can reconstruct objects' shapes more accurately than traditional methods, thereby enhancing the radar's ability to classify and understand the surrounding environment. The application of TVCS marks a significant step forward in radar-based shape estimation, especially for imaging radars used in automotive systems.

The dissertation also addresses the limitations of conventional target detection methods, particularly the widely used window-based constant false alarm rate (CFAR) detectors. Window-based CFAR detectors struggle with dynamic and unpredictable environments, which are common in road traffic scenarios. Moreover, they are unsuitable for extended targets with very different sizes, such as the ones encountered in automotive radar. To overcome this, in Chapter 6, a deep learning-based detector is proposed, trained using a newly developed dataset, RaDelft, which includes synchronized radar and lidar data. This deep learning detector outperforms traditional CFAR detectors by significantly improving the probability of detection and the Chamfer distance, especially in complex and cluttered environments. The RaDelft dataset itself is another important contribution of the dissertation, providing the research community with a well-curated, large-scale, multi-sensor dataset for further exploration and development of radar-based detection and classification systems.

In conclusion, this dissertation presents a comprehensive study of methods to enhance the angular resolution, detection capabilities, and efficiency of automotive radar systems through a combination of machine learning and compressive sensing. It provides practical solutions verified with experimental data to overcome existing limitations in automotive radar technology, particularly in the areas of angular resolution, target detection, and data processing speed. These advancements contribute to the broader goal of achieving fully autonomous driving by improving the ability of radar systems to perceive and interpret complex environments. ... Read more

As the world is getting used to 5G technology, 6G is already on the horizon. With the ultimate goal of very low latency communication and Tbps data rates, 6G supports new wireless technologies such as virtual and augmented reality and autonomous driving. In this new generation of wireless communication, high frequencies up to 100 GHz are used which causes antenna arrays to shrink to the size of a post stamp. Antenna-in-Package (AiP) is an emerging technology that integrates these antennas directly in the packaging material of IC’s. This brings the benefit of high level of integration, making beamforming IC’s an all in one system for communications. Despite this, high integration also comes with challenges. These small antenna systems generate heat on a small footprint which emphasizes the need for a low-loss, efficient system. Moreover, free-space path loss is proportional to frequency so high radiated power is needed, further emphasizing the need for improved efficiency. Lastly, space inside the IC and package material is limited which requires compact solutions for the system’s components. Literature has shown that PA-antenna co-design can reduce losses at the PA-antenna interface by matching the antenna impedance to the impedance of the power amplifier, omitting the need for lossy impedance matching networks. This method, called direct impedance matching, has shown to improve power added efficiency (PAE) in antenna systems with single radiating elements and fixed linear arrays. Nevertheless, the advantage of direct impedance matching has not yet been demonstrated for phased array antennas with active beam forming/steering.

This work demonstrates a novel analysis of PA-antenna co-design at 96 GHz using a 8x8 cavity-backed dual-polarized pin-fed stacked patch array. Two versions of this antenna array are designed in Ansys HFSS, one with an antenna impedance of 50 Ω, the benchmark, and one with a lower impedance of 25 Ω. Both designs are made on a custom package laminate stack-up and are compatible with pin-fed AiP technology. Using Keysight ADS, the antenna designs are co-simulated with an RF front-end circuit comprised of a single tone AC signal, ideal 1-64 channel power divider, ideal continuous phase shifters and realistic SiGe Class A cascode power amplifiers. In this setup, the 50 Ω reference antenna is connected to the PA’s with a matching network in between. Because the 25 Ω antenna array matches the optimal output impedance of the PA, it is directly connected. The performance of both antenna arrays are compared, with the focus on PAE and radiation characteristics. The results show that by going for a directly matched antenna the PAE of the system increases by 15.6% for a broadside beam and 30.4% with a scanned beam (θ = 45◦, φ = 45◦). EIRP for broadside and scanned beams increased from 50.3 W to 56.3 W and from 31.1 W to 37.9 W respectively. Bandwidth, gain, radiation efficiency and side-lobe levels were similar in both arrays but the 25 Ω antenna had 4 dB higher levels of cross-polarized radiation and a 4 dB stronger back-lobe behind the antenna. The advantage of higher efficiency and radiated power outweighs these drawbacks and makes direct impedance matching a good design strategy for 6G beamforming AiP technology. ... Read more

The use of radars for remote sensing in atmospheric sciences has become increasingly popular over the past few decades. Weather radars play a crucial role in measuring, interpreting, and monitoring various atmospheric phenomena. However, accurate retrieval of vertical air velocities remains a challenging problem owing to certain deficiencies in radar data and errors in deriving numerous parameters of precipitation. This research aims to develop data processing and air motion retrieval algorithms that can improve the accuracy of this task. The project proposes a robust data processing pipeline to enhance data reliability. It also conducts the classification of rainfall types and hydrometeor classes in the different regions of atmosphere for more accurate parameter retrieval. In this project criteria for the quality of air-motion retrievals have been developed. The performance of the retrieval algorithms achieved by using exponential drop size distribution (DSD) for fall velocity calculations has been evaluated based on the proposed criteria. This research also utilizes data spanning six months for performance analysis of air velocity retrieval algorithms under various weather conditions over extensive periods of time. ... Read more

Radiation pattern measurements is a critical step in characterizing antennas before they are used in any system for a specific application. To identify any defects or to acquire the true radiation characteristics of the antenna under test, densely sampled measurements are desired. However, this results in a time and cost expensive measurement process. Compressed sensing allows accurate reconstruction of radiation patterns using a reduced number of measurements. To ensure exact recovery, it is necessary to select an optimal sampling strategy as well as an effective reconstruction method.

In this thesis, the discrete Fourier transform and spherical harmonic expansion of the electric field are used to obtain a sparse representation of radiation patterns. As a variation from the basis pursuit optimization problem which is widely used in compressed sensing for antenna measurements, a sparsity enhancing weighted l1-norm minimization problem is considered. The weights are determined from prior information on antenna from electromagnetic simulations. The proposed method, after investigation with various antennas and comparison with existing benchmark results in a further reduction of number of required measurements. A near-optimal sampling technique is adopted to acquire measurement in an incoherent manner for exact recovery of the pattern. The performance of the method has been evaluated using error metrics specific to important parameters of the radiation pattern such as the gain, peak side lobe level and half power beam width. Radiation patterns with non-idealities and distortions have also been recovered with high accuracy from a small number of measurements using the proposed method. ... Read more

Increasing wireless communication requirements of data rates, capacity and coverage, and evolution and maturation of wireless equipment prompt wireless communication research insight concentrating on millimeter wave (mm-wave) frequency. However, high reflection coefficients and high path loss cause large shadow areas (e.g. behind the buildings) and poor coverage, leading to constraints on wireless connectivity and effectiveness of wireless communication. Intelligent Reflecting Surface (IRS) is a revolutionizing technology in 6th-generation mobile networks (6G), which achieves extended coverage with reducing construction and electricity costs via its characteristics of passive beamforming and proper deployment, and auxiliary of Ray Tracing (RT) facilitates obtention and analysis of channel state information (CSI) at different locations. With the objective of developing a flexible RT tool and a novel methodology for optimal IRS deployment to maximize coverage in non-line-of-sight (NLOS) areas from the BS, a new RT simulation model is built in this thesis project in accordance with measured data, leading to improved reliability and accuracy of 5.3\% maximum error in path loss. And a novel, but preliminary, weight graph methodology is proposed for tackling the IRS deployment problem for coverage extension in NLOS areas quasi-optimally. To integrate the IRS with RT simulation, a first-time comparison between metal reflectors and IRS under realistic EM effects is exploited. The obtained simulation results unveil that deploying IRS with the proposed weight graph methodology facilitates wireless coverage improvement, and the coverage probability increased from 0% to 96.23% with a threshold of -75 dBm under 28 GHz in a selected Region of Interest (RoI). ... Read more