In this Bachelor graduation project, a 16x16 Single Photon Avalanche Diode (SPAD) array is designed in 40nm TSMC CMOS for diamond single Nitrogen Vacancy center array readout. It includes an active quenching and recharge circuit (AQC), a hold-off circuit for controllable dead-time and IO electronics for off-chip communication. The chip is part of a proposed high sensitivity magnetometer based on single NV center readout with a focus on detecting cancer in biological samples. From the Quantum Integration Technology (QIT) lab, pre-designed SPADs were received to be implemented into the design together with SPAD quenching, recharge and input-output controller electronics. A SPAD model is adapted from literature for simulations of the electric behaviour of the electronics. We present a design and implementation of the Active Quenching Circuit (AQC), a design and implementation of the recharge and hold-off circuits, Input-Output (IO) interface design and implementation and the final top-level implementation ready for the next tape out. Post-layout simulations show negligible speed slowdown and distortion. The AQC has a 12ns quenching time and a 1ns recharge time, leading to a theoretical maximum count rate of 76Mc/s. The hold-off circuit has a tunable dead-time for afterpulsing reduction and 16, 16 bit parallel-in-serial-out (PISO) modules allow per-row readout of the array. The electronics are co-located with the SPADs and pixel pitch is 24m. The final chip design meets the single NV fluorescence count rate requirement of above 3 Mc/s, the 1.1x1.1 ZZ2 area requirement, the 32-pin IO requirement, the 16x16 SPAD pixel requirement and, steps have been taken to ensure acceptable crosstalk levels in the array. Finally, the SPAD array chip is designed to run on a 1GHz clock. It should interface with an FPGA that configures the hold-off circuit and reads out the SPAD status bits.

Magnetic resonance imaging (MRI) is a non-invasive tool to image the body’s anatomy and physiology, but suffers from long scan times. Compressed Sensing (CS) is used to accelerate MRI scans by incoherently taking fewer measurements and using a nonlinear optimization algorithm to image the undersampled data. Convex optimization techniques are generally used for image reconstruction. Minimizing a data fidelity term along with two regularization terms, which are a total variation (TV) based and a wavelet transform based function, is a standard procedure in CS-MRI. Regularization parameters are needed to balance the different terms, but it is impossible to know upfront what the optimal regularization parameters are to get the desired output. A consequence is that the algorithm of choice needs to be executed many times for many different values of the regularization parameters, which is a time-consuming process requiring knowledge of the algorithm.

In this work we rewrite and implement the regularization functions in a multiplicative manner by multiplying the data fidelity term with the regularization terms, thereby eliminating the need to tune the regularization parameters. Moreover, we include a region of support (ROS) mask to further accelerate reconstruction. The performance of different combinations of regularization functions and reconstruction algorithms are validated on a simulation study and various experiments on a low-field MRI scanner. This also shows the capability of CS applied to low-field MRI, which has lower signal-to-noise ratio compared to conventional MRI. Of all proposed methods, a nonlinear conjugate gradient method applied to the fully multiplicatively regularized objective function shows the most robust performance.

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.

This thesis covers two topics. The first one is signal design for accurate Time-of-Arrival estimation using a number of frequency separated signals. Rather than use a full UWB band, we will use sparse subband signals spanning the full band to construct a new virtual UWB signal. To evaluate the performance of the constructed signal, Cramer-Rao lower bound and auto-correlation are used. And given a given fixed bandwidth the number of subbands within a 1 GHz UWB channel, and optimal subbands' allocation will be found based on the evaluation results. Our results show that when three 50 MHz subbands are used to construct a virtual 1GHz UWB signal, a lower CRLB and better auto-correlation performance can be reached when subbands are close to the edges of the virtual band. However, the auto-correlation still has multiple peaks, which poses a serious challenge for accurate time estimation.The second topic is to investigate the frequency dependence of the channel impulse response of subbands with different frequency separations. We propose a covariance calculation method to determine the frequency dependence which changes with frequency separation.To validate the method, different artificial UWB channels with distinct paths are given. The results show that covariances between the subband CIRs stay at a high level when measured at the direct path and the majority of interference caused by other paths can be eliminated by a wider bandwidth subband. Given UWB channels measured from 5 to 10 GHz with a link-budget of 120dB, the frequency dependence of the direct path and reflections are determined, different bandwidths and frequency separations are used, and the results show that the channel impulse response of the subbands will become different when measured at different center frequencies, where the difference increases with an increased frequency separation of the subbands. The correlation of the direct path is maintained over larger frequency distances than that of reflected paths.

Ground clutter signal is a kind of unwanted echoes in target detection radar system, which are normally reflected by ground surface, ground-based objects, and obstacles. It can be collected and characterized in polar coordinates in terms of range and azimuth. By using polarimetric based algorithms, including them single channel detector, the span detector, the power maximization synthesis (PMS) detector, the identity likelihood-ratio-test (ILRT), the polarimetric whitening filter (PWF), and the optimal polarimetric detector (OPD), the target detection radar system can distinguish the characteristics and diversity of multiple targets.
In this thesis, a polarimetric radar simulator to generate multi-channel polarimetric signals with specific statistical characteristics has been developed and validated in the simulation. In the measurement, a new noise-based equalization for all polarimetric radar channels has been proposed and tested, which improves the reliability and accuracy of the polarimetric information. After noised-based calibration and model-based decomposition of the polarization covariance matrix, with regenerated measurement slow-time data, a variety of targets in heavy clutter with signal power comparable to the target are detected by polarimetric algorithms in the environment of strong clutters. Starting from numerical simulation and comparison of different detector algorithms, this work has validated the feasibility and accuracy of each detector in realistic scenario. The measurement result agrees with the simulation result that with the use of radar polarimetric information as a priori knowledge, target detection can be improved
by polarimetric detectors.

The displacement of a loudspeaker cone is not linearly proportional to the voltage/current at its input provided. This is due to electrical and mechanical limitations. The distortion is more noticeable at lower frequencies. This report is one of two reports, that will explain how to minimize this distortion. The motion of the loudspeaker cone will be measured and compared with the input voltage. This way the error in the output can be found. Finally, a system will adjust the current through the voice coil to decrease this error. An accelerometer is used as a sensor and the ADAU1777 is used to compensate for the error in output displacement. Finally, a voltage to current converter is used to convert and amplify the output voltage from the ADAU1777 into a current signal to drive the loudspeaker. This report focuses on the voltage to current amplifier. To begin the design, a problem definition and program of requirements will be given. Usually, loudspeakers are voltage driven. This report begins by first explaining why a current-driven loudspeaker delivers less distortion. Then an attempt is made to build a single operational amplifier (opamp) design that meets the requirements for a real load. For the single opamp design, a simple version of the operational transconductance amplifier (OTA) will be compared against a Howland model based on noise performance and circuit analysis, where it will be shown that the simple OTA has a larger signal to noise ratio and does not need to meet any additional criteria to maintain a high output impedance. However, the simple OTA still does not meet the Signal to Noise (SNR) . The simple OTA also did not meet the maximum Total Harmonic Distortion (THD) at 800 Hz. Then, a two opamp design will be constructed using a composite configuration to increase the SNR and decrease the THD. This design does meet the requirements given, at least for a purely resistive load. Then an electrical model of a loudspeaker, which has a complex impedance, will replace the resistive load. Now the composite design for the resistive load needs to be adjusted for the inductive characteristic of a loudspeaker at higher frequencies. The resulting design has a THD of 0.000427\%, a PM of 51$\degree$ and a SNR is 100.92 dB. All results will be given as simulations, because the current pandemic (COVID-19) does not make it possible to physically construct and measure this model. Micro-Cap 12 is used for circuit simulations and Slicap for circuit noise analysis.

Recent advancements in Multiple-Input Multiple-Output (MIMO) radar techniques has created a paradigm shift in the overall radar technology to increase degrees of freedom in multi-function radar capabilities. The underlying principle of MIMO transmissions is to transmit independent waveforms from each antenna-element which do not interfere with other transmitted signals and establish wide illuminations which create multiple received signals back-scattered from the same target under observation to provide more information on the target aspects. One other principle is called colored transmission, by simultaneously radiating specific waveforms from each antenna-element/sub-array in different directions to achieve ‘space-time coding’. This can be explained as colored spatial distribution (multiple coded beams to probe the radar environment) instead of the white spatial distribution (single wide beam), such that the transmitted signals are now function of time as well as space. Recently, a novel multi-channel waveform agile demonstrator namely ASTAP (Advanced Space-Time Adaptive Processing) radar system has been designed and developed in Microwave Sensing, Signals and Systems (MS3) group. It consists of eight transmit channels with a single receive channel and hence can also be called as co-located Multiple-Input Single-Output (MISO) radar. The ASTAP radar system is capable of generating and transmitting independent waveforms via multi-channel Arbitrary Waveform Generator(AWG) simultaneously . However, the transmission of different coded waveforms with a radar system such as ASTAP demonstrator has some major challenges to be addressed first. These challenges include the impact of AWG on digital-domain generation of waveforms including limited-bits quantization errors, time- and phase-skew ( and/or jitter), influence of high-frequency up-conversion hardware components such as RF mixers (as non-linear devices) and antenna dispersion effects. First novelty of this thesis work is to investigate the influence of these system imperfections on waveform transmit ambiguity functions, waveform orthogonality in MISO transmissions, received signals separation and beamforming process for the synthesis of target azimuth distributions. It follows that an end-to-end system-level calibration to compensate these system imperfections on transmit side for different waveforms also serves as the second novelty of this thesis work and has been demonstrated with ASTAP radar system. For system performance analysis and beamforming applications, Over-the-Air (OTA) channel measurements have been done to compensate the ASTAP hardware distortions significantly and obtain the near-ideal waveform responses. Furthermore, it is investigated that to what extent it is possible to separate signals corresponding to each transmit channel from the composite received signal in a single receive channel. This study is extended further to generate and transmit two orthogonal beams occupying the same frequency band simultaneously and the corresponding transmit radiation patterns are recovered from the composite received signal matched filtering. Finally, conclusions along with future aspects and recommendations have been discussed.

For radar applications, MIMO is often used. The problem is that the amount of available transmit- and receive channels is limited. If each channel is connected to a single antenna element, the effective aperture is small, and therefore the beamwidth is large and the angular resolution is limited. In this thesis, a novel scheme is proposed that circumvents this problem. More details are given in the full thesis. The result is an array that covers a reduced part of the visible space, with discrete beams that have improved beamwidth. The system gives the possibility to improve the angular resolution at the cost of field of view. A CST-model is made to verify this concept. In the specific example given in this thesis, the half-power beamwidth is improved from 8.4 degrees at broadside, to 5.7 degrees at broadside. At the same time the coverage is reduced from a theoretical ±90 degrees, to ±50 degrees.

Stratiform precipitation is one of the most important precipitation systems in the mid-latitudes. To gain understanding of the melting layer, which is part of a stratiform precipitating system, an algorithm which is able to select melting layer data from large datasets is required. The goal of this thesis work is to create and deliver a program which is able to performthis work. For development the available data was separated into three groups: no melting layer, only melting layer and the rest. Based on the second group, typical melting layer signatures are analysed. In particular the different signatures (reflectivity, polarimetric and Doppler) occur at different heights. Based on the knowledge gained from the analysis and focussing on the reflectivity and polarimetric signatures, three different approaches were taken to detect and characterise the melting layer fromthe data. The first approach is based on an existing method in literature, this approach acts as the reference method. The second approach is based on image processing techniques, while the third approach is based on machine learning techniques. The second approach is later abandoned because of limitations of the techniques investigated. The third approach, machine learning, is the main contribution of this thesis work. For analysis of the performance of the different algorithms an annotated test dataset is created which represents the entire dataset. The performance is determined by the melting layer detection probability using a confusion matrix and by the determination of errors of the melting layer boundaries. The reference method proved to show an extremely low false positive rate (0%) on the test dataset. This means that if the method detected a melting layer, it is almost certain that there is one. The overall detection probability was 80%. The method fails in detecting the melting layer when the peak reflectivity is below the threshold (30dBZ) used in the method. The detected upper melting layer boundary of the reference method is on average 365 m lower compared to the ground truth. The lower boundary is on average 149 m higher than the ground truth. This means that the reference method only selects a part of the entire thickness of the ML. The correlation between the lower boundary and the ground truth is higher than the upper boundary (0.987 vs. 0.965). The proposed machine learning method has a detection performance of almost 94%, which is higher than the reference method, but some false positives occur (3%). The upper boundary is on average 20 m above the ground truth, while the lower boundary is 66 m lower than the ground truth. The machine learned method thus overestimates the thickness of the ML. The correlation between the lower boundary and the true boundary is 0.976 and the correlation between the upper boundary and the true boundary is 0.966. The upper boundaries of both methods have a very similar correlation, while the lower boundary of the machine learning method has a lower correlation. However, despite the lower correlation of the lower boundary and the introduction of false positives, the machine learning method is an improvement over the reference method since it has a significantly higher detection probability and it captures the entire thickness of theML much better. It is therefore suited forML analysis.

Micro-Doppler patterns of multi-propeller drones measured by radar systems are widely used in the classification of different drones, since the micro-Doppler patterns illustrate the velocity and motion properties of the drones. However, on this topic, there are a few issues the current researches have not tackled yet, and these will be discussed in this presentation. First of them is the lack of mathematical description of the micro-Doppler patterns. and most research works are based on real measured data so far. In this thesis, an EM backscattering model in HH plane of drone propeller is developed, simplifying the propeller’s geometry structure as a few cylinder thin wires. Radar signal model and micro-Doppler model are subsequently developed for the thin-wire propeller model when it is rotating. Second, most current researches focus on the micro-Doppler patterns achieved in short CPI cases that are valid for radar systems with PRI much shorter than the rotation period of the drone’s propellers. In this thesis, the drone micro-Doppler patterns in long CPI circumstances are investigated. Features are proposed to characterise the amplitude and frequency distribution of the simulated micro-Doppler spectrum. Applying these features to SVM gives good classification accuracy for the simulated micro-Doppler data. Third, most researches at present are carried out in short range for static or stable hovering drones, while from a practical point of view, it is also of great interest to investigate the drone micro-Doppler patterns in long range and dynamic scenarios. In this thesis, the micro-Doppler patterns of different drones at a distance of 9 kilometres are achieved by S-band radar in long CPI circumstance. Applying the previously proposed features to the real measured micro-Doppler spectra to SVM gives good classification accuracy for drones in hovering and manoeuvring flight modes.

Traditionally, a geographical area can be monitored via land based communication systems or spaceborne remote systems for disaster prevention. However both methods have their respective limitations in terms of resolution, accuracy and real time observation. A new suborbital constellation concept offers an independent unit device which will be attached to an aircraft and use commercial flights as means of monitoring forest fires or forecasting solar energy. The shape of the fuselage can be used to redirect the creeping wave generated from the antenna away from the aircraft’s surface, towards the satellites for communication purposes. This research aims to determine an optimum antenna placement on the electrically large body in order to satisfy efficient communication with ground and satellites from the same location on the aircraft.
The quarter wavelength monopole operating at 1.575 GHz in L1 band for the GPS communication and a patch antenna with dielectric constant of 2.2 for the substrate operating at 800 MHz for the GSM communication are selected. Various algorithms and approaches are investigated to perform comparison of accuracy for results and computational resources between different simulation techniques namely Multi-Level Fast Multipole Method (MLFMM), Physical Optics (PO) and Uniform Theory of Diffraction (UTD) using a modern computational electromagnetic numerical platform FEKO. The simulation results show that the optimum antenna location on a Boeing 737 is found to be at the bottom of the aircraft in the conical section of the tail 30.95 m away from the tip of the nose in the longitudinal direction. In addition to that, this work provides a unique perspective to system engineering for using aircraft as a land monitoring station.

Traditionally, antenna design involves optimizing the design parameters such as gain, minimum sidelobe level, scan range, half power beamwidth, for a particular application. Since 5G systems are pushing the boundaries for cellular communications by introducing unique challenges such as multiple beam antennas, beamforming etc. in the field of antenna design. Although separate studies do exist on these challenges, the concept antenna
synthesis using different disciplines is relatively new. This work presents a 5G simulation model using a recently proposed hybrid beamforming technique employing cosecant power flux equalization in elevation plane with digital beamforming in azimuth plane. Various beamforming algorithms in azimuth domain are investigated for concurrent users sharing same frequency spectrum and their impact on system performance such as signal to interference plus noise ratio (SINR) is statistically analyzed. Moreover, the impact of antenna sidelobe levels on system SINR performance is investigated in detail. The simulation results shows that the cosecant subarray hybrid beamforming performs better than traditional hybrid beamforming in terms of SINR for cell edge users. In addition to that, this work provides a unique perspective to system
engineers for intuitively analyzing the impact of antenna system on communication link and to derive design requirements that would be crucial in the antenna system synthesis

Every year about 1.25 million people die as a result of road traffic accidents. Besides the traffic on the road increases every day, including the environmental impact due to the corresponding traffic emissions. Autonomous driving could be a unique opportunity to increase these traffic safety, traffic flow efficiency and to reduce emissions in future. In order to operate reliably and accurately, autonomous driving vehicles and autonomous features require an accurate perception of the infrastructure and other road users in the surrounding. Multi-object tracking is the process concerned with the estimation of the states of the objects in the environment, given the noisy measurements from the sensors. Besides the estimation of the states, it is also necessary to estimate the classification of the objects e.g. for a correct situation analysis and path planning. Classifiers are used to detect and classify objects from image frames, however the classification is sometimes incorrect or uncertain. This results in a decrease of tracking accuracy and an incorrect classification in these classification uncertain conditions. The contribution in this work is, by keeping the classification uncertainty in the tracking approach and using it in all steps, to jointly improve the tracking accuracy as well as the classification.