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.

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.@en

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.

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.

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.

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).

The driver’s safety is one of the problems that is considered by modern vehicle technologies. Many accidents occur due to extreme weather conditions, such as snow or freezing rain. Such weather causes decreases in the friction of the road surface, which cause danger for drivers in this kind of area. To improve the driver’s safety, ADAS (Advanced Driver Assistance Systems) can be used to recognize different road surface conditions and warn the driver to avoid them. To do this, ADAS needs to perform classifications on the surface, and radars operating above 77 GHz have shown potential for this kind of task. The polarimetric information of the surface is needed as the prior knowledge for radar to perform the classification properly, which can be obtained from the scattering of the surface. The scattering properties of the asphalt surface at 76 GHz is a complex problem and it is difficult to have a comprehensive solution. In the existing research, there is no research that used a full-wave numerical approach with a simulation model for the kind of problem in the 76 GHz frequency band. In this thesis, a numerical model is developed in the electromagnetic software suite FEKO to simulate asphalt surface scattering. The model considers surface roughness, dielectric medium, source polarization, and incidence angles for scattering from the asphalt surface. Based on the model simulations, statistical analysis is performed using the Monte Carlo method. The results of the model in the case of an RMS height of 2 mm and correlation length of 2mm show that for a very rough surface, the RCS of the surface decreases as the incidence angle is larger, and the value of the two polarization components is close to each other. By verifying the numerical model with SPM theory, it has been shown that the designed model can give accurate results in the designed scenarios, which proves that the designed model can be used for investigate rough surface scattering properties.

The mm-wave operational band (24-30 GHz) is becoming increasingly important for various practical applications. However, at such high frequencies, the issue of propagation losses arises, emphasizing the necessity for these systems to achieve high radiation efficiency. Dielectric Resonator Antennas (DRA) are promising candidates to replace traditional radiating elements like patches since they do not suffer from conductor losses and have a radiation efficiency of over 90% when suitably excited. Additive manufacturing has emerged as a promising technique for producing DRAs due to its numerous benefits, such as the ability to fabricate complex shapes and structures, rapid prototyping, and reduced waste. Additionally, 3D printing can enable the incorporation of varying permittivities within the DRA, further enhancing its performance. The influence of such permittivity profiles in DRAs, particularly in finite phased array setups, is yet to be thoroughly investigated. It is important to understand the impact of this technique on the mutual coupling, cross-polarization, and scanning performance of the array. In this work, we discuss the theory behind the radiation characteristics, the modelling of the DRA designs and the analyses of these designs based on performance criteria such as bandwidth, coupling, gain, cross-polarization and axial ratio bandwidth. This thesis demonstrates for the first time, to the best of author’s knowledge, that the incorporation of permittivity profiles in mm-wave DRAs can improve the bandwidth by 7%, reduce the cross-polarization (at broadside) by around 3 dB and improve the axial ratio bandwidth by around 10% compared to single-permittivity DRAs. Furthermore, it is also shown that in an array environment, the active S-parameters of the elements are better matched across a wider frequency band, upon scanning from 0 to 45 degrees, when a permittivity profile is used.

This thesis project is centered around the retrieval of meteorological parameters using a fast-scanning phased array radar, specifically targeting precipitation-like objects such as raindrops. The main objective is to transform radar data into variables that accurately characterize precipitation. This endeavor involves addressing various challenges, including extracting meteorological object-related variables, mapping targets from noisy radar measurements affected by phase noise, and resolving the issue of Doppler aliasing.
In this report, these challenges are tackled by introducing a range of techniques and algorithms designed to enhance radar data analysis and validate the proposed methods. The most commonly derived radar parameters for meteorological targets, known as Doppler moments, are extensively discussed, including reflectivity, mean Doppler velocity and Doppler spectrum width. An exploration of the circular calculation of Doppler spectra moments is conducted, providing valuable insights into the velocity distribution of radar targets. By analyzing higher-order moments, the characteristics and dynamics of the targets can be better understood, leading to improved target identification and tracking. Additionally, a novel circular variance-based target mapping technique is proposed to map targets from noisy radar measurements effectively. This approach proves particularly well-suited for scenarios where traditional reflectivity-based methods fall short. Apart from the circular variance-based technique, this thesis explores reflectivity-based target mapping approaches that offer enhanced methods for identifying and classifying different target parts. Two pipelines are proposed: the morphology-based pipeline and the entropy-based pipeline. Through simulation and real-world data analysis, the pros and cons of each pipeline are carefully evaluated. The second pipeline demonstrates significant benefits in classification, allowing for a more detailed and accurate representation of radar returns. It effectively distinguishes point targets, extended targets of interest, global noise, and phase noise, enabling a more comprehensive analysis of radar data and enhancing the interpretation of detected targets.
Furthermore, the critical issue of Doppler dealiasing is thoroughly investigated, addressing the problem of velocity ambiguity caused by the Nyquist limit. Three approaches are compared: gradient-based, existing advanced technique UNRAVEL, and optimizer-based. Based on the evaluation of the simulation model, the "ParticleSwarm" optimization approach is selected as the most effective in enhancing velocity measurements in severe weather conditions.
Throughout this report, experimental results are presented, findings are discussed, and recommendations and suggestions for future research are provided. The proposed techniques and algorithms have undergone rigorous evaluation and validation using both simulated data and real-world radar measurements. Overall, this research contributes
valuable insights into radar data processing for meteorological applications, offering improved accuracy and reliability for various weather-related analyses and forecasting tasks.

Hybrid beamforming (HBF) architecture provides promising trade-offs between the system performance and computational/hardware complexity in practical implementations of millimeter wave (mm-Wave) massive multiple-input multiple-output (mMIMO) 5th generation (5G) mobile networks compared to its fully digital beamforming (DBF) counterpart. In this thesis, we investigate the future applicability of deploying hybrid beamforming architectures with subarray beam pattern shaping for mm-Wave 5G base stations in spatially heterogeneous user distributions and different propagation scenarios. We propose HBF structures with a cosecant-squared pattern and a flat-top pattern as well as their HBF and DBF benchmarks. In addition to the uniform user distribution, three non-uniform user distributions, i.e., the near-site distribution, the cell-center distribution, and the cell-edge distribution are proposed to represent the traffic flow and mobility of users due to festivals and holidays. We evaluate the performance in a novel 5G new radio (NR) system-level simulation (SLS) model. Numerical results show that the HBF architecture with a cosecant-squared subarray beam pattern is more robust against differences in spatially heterogeneous traffics than the flat-top HBF and benchmark HBF under the line-of-sight (LoS) propagation scenario. Under the non-line-of-sight (NLoS) propagation scenario, more deterministic environment information and radio channel modeling are required to improve the system performance of the shaped HBF beamforming technique.

With the development of the fifth generation (5G) technologies and beyond, the requirements for future wireless communication systems become more stringent, which demand novel antenna design at millimetre waves. The most commonly proposed array topology for 5G usage is square 8x8 or 16x16 microstrip patches with the full digital beam-forming method. Such designs result in massive complexity, high power consumption, and heat generation, especially with the full digital beam-forming method. To reduce design complexity and power consumption, the hybrid beam-forming is proposed, in which the linear subarrays are used with a fixed beam pattern in elevation, and combined in an array with digital beam-forming to realize the beam scanning in azimuth. Recent studies in the MS3 group at TU Delft have demonstrated that the series-fed subarray can be used in hybrid beam-forming and can realize any desired pattern by independent control of amplitude and phase on each unit cell. However, the main drawbacks of the series-fed arrays are the radiation beam squint and pattern distortion under different frequencies, which can limit the radiation bandwidth. Therefore, it is necessary to investigate the design of the series-fed antenna arrays to reduce the beam squint and pattern distortion. In this thesis, the performance of series-fed antenna arrays radiating the csc2-shaped beam pattern is investigated in terms of beam squint and pattern distortion. The RMS value is used in this thesis to quantify the pattern distortion in series-fed arrays and is used as a performance metric. Firstly, seven types of edge-fed series-fed subarrays are designed and optimized in CST. Their performance in terms of beam squint and pattern distortion is evaluated with and without considering the mutual coupling effect. Then, the subarray with the most stable radiation pattern over the evaluated frequency band (25 GHz - 27 GHz) is selected. Following this, the center-fed series-fed proximity-coupled subarray is designed aiming to reduce the amount of beam squint while having a stable radiation pattern. The performance of the center-fed subarray is compared with the edge-fed configuration in terms of the beam squint and pattern distortion. The simulation results show that the center-fed configuration can significantly reduce the beam squint but can increase the pattern distortion. Finally, the array of center-fed series-fed proximity-coupled subarrays is designed and simulated in CST. The results show that the array is able to reduce the beam squint (within 1°) while having a reasonable pattern distortion (around 20% difference compared to the synthesized pattern) within the frequency range from 25.8 GHz to 26.5 GHz.

The increasing use of automotive radars on roads has led to a significant issue of mutual interference between them. To better understand the characteristics and impact of this interference, it is necessary to perform analytical investigations of FMCW radar-to-radar interference. This thesis presents analytical models for FMCW-to-FMCW mutual interference in ideal conditions, categorizing them into four cases based on different parameters: fully synchronous, generally synchronous, periodically asynchronous, and aperiodically asynchronous. The impact of these types of interference is evaluated on the probability of detection and false alarm probability using CFAR.

Since 2018, a race on 6G technology between many countries and institutions has started.
As 6G mobile communication utilizes sub-THz frequency band, the antenna packaging requires higher level integration. Antenna in Package (AiP) is seen as a promising solution when it comes to the integration problem. At 100GHz operation frequency band, AiP is not only feasible but also mandatory for a high-density interconnect and efficient IC-antenna integration. Current packaging technologies such as LTCC, HDI, and E-WLB, and their feeding concepts limit the possibilities to achieve an optimum
for the given silicon and antenna configuration, such as the complexity of the feeding network, the realizable thickness of metal or substrate material, and so on. To overcome these limitations, a novel high definition fan-out technology is investigated. This high
definition fan-out technology enables complex-shaped vertical interconnects. Yet there is a lack of research focused on the performance of such interconnects as well as the impact of its manufacturing process on the RF performance.

This thesis work is aimed to provide a scope of how the high definition fan-out technology benefits the performance of an AiP by applying coaxial structure as an antenna
feeding path in such a package. This new feeding concept is aimed to optimize the interface structure between the RF front end in silicon and the antenna array in the same
package, to reduce the routing size, reduce the insertion loss between the antenna array
and the silicon interface, and increase the isolation between the two adjacent feeds. An
initial model is fully parameterized for performance improvements via tuning. A benchmark model built up by the traditional laminate technology with vias is compared with
the performance of the proposed feeding structure regarding matching, insertion loss,
and port isolation. The presented model achieves 0.25dB insertion loss, 25dB return loss
and 80dB isolation. Compare to the benchmark model, on average, the model from the
high definition fan-out technology has significantly decreased the insertion loss, and
increased the channel isolation, while maintaining a similar return loss.

The impact of this high definition fan-out technology’s manufacturing process on the
model performance is discussed through parameters’ sensitivity analysis and system
robustness. The robustness analysis shows this feeding system has acceptable reliability
if the manufacturing error can be restricted within 10%, and it has good reliability if the
manufacturing error can be restricted within 5%.

In the Roadmap of 2025, EURO NCAP has announced that it will reward car manufacturers that include Child Presence Detection (CPD) technologies in their vehicles starting from 2022. Additionally, seat belt detectors are currently based on pressure sensors which can be falsely triggered with large objects placed on the seats. By basing the detection of people on human vital signs presence instead, these errors can be avoided. Therefore, in recent years, there has been an increasing need to find a solution for multiple people detection and localization in vehicles for both CPD and seat-belt reminder systems in the automotive industry.

With Ultra Wide-Band (UWB) radar, non-invasive human detection is possible through the identification of vital signs characteristics in the radar data. This work aims to improve existing literature by developing a network of UWB radars to perform multiple people detection and localization.

Specifically, an algorithm for de-centralized vital signs detection is proposed, based on the analysis of a novel model for radar signatures of vital signs. Additionally, a centralized association block is developed to fuse the detections from all radars using machine learning-based cost-matrix computation. The performance of the proposed processing pipeline is tested experimentally with a multistatic radar network. A simulation framework is developed for radar data generation to evaluate the results obtained in the experiments, and to propose variations on the evaluated radar topologies.

It can be concluded that the detection and localization of humans in the environment is possible with the proposed framework, with localization RMSE of 16cm for single and double target scenarios. The distribution of multiple focus points and the introduction of bistatic radars enhances the detection, and thus localization, w.r.t. current methods based on monostatic radars and MIMO radars.

An exponential growth in the mobile communications industry over the past decade has raised concerns over it’s energy consumption, especially in light of the current climate crisis. 5G technologies in particular exhibit features that imply high power consumption, such as the densification of 5G base stations. Implementing mitigation measures requires accurate a priori knowledge of the power consumption of 5G array architectures in various scenarios; however, there are large gaps in the literature on this topic, and existing power consumption models for 5G array architectures only address a limited scope of use cases and array topologies. Recent works have focused on the energy efficiency of array architectures, but not on the actual amount of power being consumed.

In this thesis, an integrated system-level power consumption model is devised for 5G base station multi-beam transmitter topologies and beamforming schemes, which accounts for the use cases and architectures missing from the literature. The model is then applied to novel use cases in the enhanced urban Mobile Broadband (eMBB) scenario to obtain the estimated power consumption per component, per array and per user for five different beamforming schemes. A thorough parametric analysis is conducted for the optimality and trade-offs of each use case. Recommendations are made on the optimal topology, beamforming scheme and front-end technology from a power consumption perspective. Initial results show that the choice of technology, architecture and topology can lead to an improvement in the per-user power consumption of 41-80%.

Powered by technological advances, commercial drones have wide applications in areas such as photography, search, and rescue. However, the popularity of drones has raised technical and safety challenges for drone management and supervision. Traditional detect-before-track sensing approaches have reduced performance due to drones' low radar cross section, low velocity, and high maneuverability, and a long integration time is required to perform the spectrogram-based drone characteristics estimation.
In this thesis, a coherent electromagnetic scattering model of the drone is applied to the track-before-detect algorithm to provide a better detection performance in low signal-to-noise ratio cases and jointly estimate the dynamic state of the drone, including range, velocity, rotation frequency, and signal intensity from drone body and rotors. With the help of tracking results, a fusion of spectrogram-based characteristics estimation approaches is developed to estimate the constructional parameters of the drone, and a novel model-based number of rotor and multi-rotation frequency estimation method is proposed. The algorithms are first verified with simulation data, achieving 85%-95% detection probability at the SNR level below 5 dB and an estimation accuracy up to 96% in the number of rotor estimation. The algorithms are also validated with the experimental data, achieving agreement with the estimation results.

Direction of Arrival (DoA) estimation is an important topic in radar application and has significant importance for advanced driver assistant systems in the automotive industry. While there is an increasing need for higher resolution and increased target detection and DoA estimation performance, such improvements often require increased hardware cost, complexity and size. With the increase in computational power of modern systems, Compressive Sensing methods have become more attractive as alternative methods for DoA estimation to the established ones, which often rely on uniform linear arrays (ULA) and the acquisition of multiple snapshots to provide good performance. Compressive Sensing methods have been shown to fit very well into the DoA estimation framework and have the ability to use far fewer snapshots, provide super resolution capabilities and by nature, utilise sparse spatial sampling, i.e. sparse antenna arrays. The latter point is the key incentive of this thesis.

Specifically, Bayesian Compressive Sensing (BCS) which in addition to point estimates also provides measures of uncertainty is used in this thesis, to generate and use sparse
linear array structures for DoA estimation. In particular, the entropy of the recovered coefficient vector is reduced in each step. Two array generation algorithms are proposed
building on the same concept to generate sensor arrays for the consideration of a uniformly spaced, linear grid of possible sensor locations and for a Multiple In Multiple Out
(MIMO) array setup. Utilising sparse arrays with BCS has the potential to reduce the hardware complexity of the circuit board, reduce energy consumption and heat generation, as well as ultimately saving costs in production and operation.

The proposed array generation algorithms are first tested and assessed with simulated Frequency-Modulated Continuous-Waveform (FMCW) radar data, where it is shown that
the generated algorithms achieve good estimation and detection performance with a heavily reduced number of sensors compared to their fully filled template arrays. Moreover, they are shown to outperform randomly generated arrays in most cases that have been studied. To add practical insight, the generated antenna arrays are tested with measured data that has been captured in two measurement campaigns with a Texas Instruments Cascade Evaluation board, featuring an 86 element virtual ULA, which has been used as the grid of possible sensor positions for the array generations. The simulated results are affirmed by the measured data, although more sensors tend to be required depending on the clutter present in the scene. It is shown, that BCS can work very well with the proposed, heavily sparse arrays tested on both simulated and measured data, which translates directly to a possible reduction in required hardware antennas. Although in this thesis the possible sensor positions have been confined to a grid of positions spaced by half the wavelength, it is easily possible to extend the procedure to a more finely divided search space.

Drone detection and tracking systems are nowadays a requirement in most public, private and political events, because of the increasing risk of unintentional or malicious misuse of these platforms. Moreover, in order to ensure adequate protection, full spatial coverage is a must for every such system. However, the research literature focuses on staring radars that have a limited field of view, but which yield rich target information via time-frequency distributions that facilitate the target recognition task. In this thesis, surveillance radars that offer full spatial coverage are presented, albeit their usage for classification is made more complex because of the rotating nature of their antennas which limits the dwell time on targets.

Additionally, due to the incredible fast growth of the drone market, novel counter-drone radars that are able to jointly localize and classify small targets while on-the-move now represent a highly in-demand remote sensing system. Nonetheless, surveillance sensors anchored on moving vehicles are a brand-new technology that is currently being developed. This work therefore investigates surveillance systems in a novel scenario, and presents the technological challenges alongside the proposed solutions to achieve reliable object detection via grounded counter-drone radars on-the-move. Specifically, the required pre-processing steps to remove the clutter from the data while the radar is rotating and moving on the ground are developed and discussed.

In the end, the joint detection and classification problem is traditionally solved separately by different algorithms due to the computational complexity of the task. This thesis project presents a novel framework that localizes and labels drones in an unified pipeline under the umbrella of object detection via computer vision, and that is able to operate while being static or on-the-move. Thus, an end-to-end radar data processing architecture that is robust against homogeneity constraints and based on You Only Look Once (YOLO) model is used to perform object detection in real-time. In brief, this work opens new avenues towards multi-class and multi-instance plot-based target detection and classification by transferring cross-disciplinary algorithms from computer vision into remote sensing.

Nowadays, the aging problem is shaking the root of the healthcare system in many countries, an automatic human activity recognition (HAR) is seen as a promising solution to that problem. In particular, radar-based HAR attracts people’s attention thanks to its respect for privacy and functionality in poor lighting conditions. With a lot of research paying attention to this topic, there is still a lack of conclusive and practical methods. In particular, it is realized that dynamic motions at large aspect angles close to 90◦ or static postures have not been investigated in-depth as a part of the radar-based HAR problem. To extensively investigate this type of problem, we propose to use mm-wave FMCWMIMO radar to obtain accurate information of the human subject.

This thesis work aims to fully exploit the six dimensions of information provided by an imaging radar: range, azimuth, elevation, velocity, power and time. Two complementary data representations- point cloud and spectrogram- are utilized to represent these dimensions of information. A signal processing flow is implemented to generate the desired data representations. A hierarchical pipeline consisting of three cascaded deep learning-based classification modules is proposed to process the input data. Particularly, human orientation classification is achieved through the so-called "T-Net" network learning the geometric distribution of point clouds. The positive contribution of each module in the proposed pipeline is validated via an ablation study. The superior performances of the proposed pipeline are also established by comparing with those of the state-of-the-art baselines. The robustness of the proposed pipeline concerning a noisy environment is also discussed. It is also presented that the size

Over the years in the automotive industry, the use of radar sensors in advanced driver assistant systems (ADAS) has been increasing, shining a light on various safety and security concerns to be dealt, that come along with it. The performances of existing sensor technologies such as camera, LIDAR and GPS can be very well complemented by the use of millimeter-wave radar due to its robust performance in various conditions. In assisted or autonomous driving tasks, localization of targets and their height information is important to be determined. Since height information is lacking from automotive radar systems without elevation measurements capabilities, this remains a challenge to be worked on. In the application discussed in this thesis, using the radar specifications providing a high range and velocity resolution, the multi-paths can be resolved in the range-Doppler domain and be used for height estimation techniques. The automotive applications for which short and ultra-short range radar systems are used, require a high range and velocity resolution. In this thesis, a low complexity and fast target height classification algorithm is presented using a FMCW radar.

In the process of having a quick response automotive radar system, low latency DoA estimation technique was employed. To estimate the height properly, knowledge of the ego-velocity is needed. A histogram method can be used to roughly estimate the ego-velocity. The ego-velocity estimate can be refined using an Extended Kalman filter that tracks targets that are supposed to be annotated with height information. Using the vertical doppler beam sharpening technique, the improved ego-motion estimate helps in estimating the elevation angles of the targets and therefore, their corresponding height. An overview of the height estimation process is explained to provide a real time mapping of the target in 3-Dimensions, showcasing the improvement in ego-motion estimation leading to an elevation angle estimation. Simulations and experiments are carried out to validate and verify the algorithm. The results show promising results and also reveal potential improvements and the need for more simulations and experiments to mature the concept further.