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.

In radio astronomy (RA), one of the key tasks is the estimation of the celestial source powers, i.e. imaging. To maximize the performance, it is crucial to optimize the receiver locations before the construction of a telescope array. However, although system calibration is an integral and crucial process of imaging, it has rarely been addressed for RA sensor placement problems previously. This motivates us to investigate whether incorporating calibration can result in better array designs. In this thesis, we focus on the calibration of the sensors’ complex-scalar gains in particular, which are treated as nuisance parameters for the image estimation. The associated Cramer-Rao bound (CRB) is derived and employed as the design criterion. The nonlinear CRB-based sensor placement problem is cast as an NP-hard combinatorial optimization problem, and we adopt two approaches to solve such by approximation: (i) greedy algorithm and (ii) convex optimization with semidefinite relaxation. The former is chosen for simulations due to its good performance and lower computational complexity. Extensive simulations shows that compared to the calibration-excluded design, the proposed one only provides slight improvements to the imaging quality. However, the proposed array demonstrates the potential of accelerating the convergence of the gain estimation procedures. Through further investigation, we conclude that the lack of imaging quality improvident can be a consequence of the gain and image being near-orthogonal parameters.

In the field of automotive technology, the last decade has seen a rise in projects focusing on self driving vehicles. With this renewed interest from the industry and academic world, new solutions to improve the reliability and safety of these vehicles are required. In this thesis, a new method for information sharing between different vehicles is discussed. This method makes use of radar devices, already in use in the automotive sector for obstacles detection. More specifically, a communication channel will be created between two frequency modulated continuous wave (FMCW) radar devices, using the same waveform utilized for radar sensing. This work will focus on the obstacles to overcome in order to achieve correct data transmission between two radar devices. Different solutions will be evaluated, and a working system, achieving data transmission, will be implemented using radar evaluation boards provided by NXP semiconductors.

Natural or man-made disasters can have a drastic impact on social, economic and environmental aspects of an affected population. Specifically, earthquakes are one of the most potent natural hazards, which cause a disproportionate amount of fatalities, primarily due to a) unexpected building collapses, b) restricted or limited access to basic amenities and c) potential hazards following earthquakes such as landslides, tsunamis etc. It is crucial to have an overview of the infrastructural damage caused following a disaster for search and rescue services to assess the extent of the damage. For the purpose of this research, Sentinel 1 imagery is used to map the building damage in an urban area after a disaster. A combination of parameters such as persistent scatterers, pixel amplitude and phase is used with a timeseries of full-resolution and spatially averaged radar images. Points that are stable in amplitude over a long timeseries, also known as Persistent Scatterers, are extracted from a stack of full-resolution images. The amplitudes of persistent scatterers, along with amplitude and coherence of pixels derived from a stack of spatially-averaged images, are statistically analysed to check the trends of the parameters pre- and post the disaster. A change detection algorithm is applied to this stack in order to localise the areas of building damage. The results are superimposed on Google Earth for easy interpretation using a graded damage scale. The analysis shows that exploiting the persistent scatterer amplitudes in the manner used in this research provides a novel way of locating building damage. This technique can be used effectively in urban areas. Using a combination of pixel amplitudes and coherence along with the persistent scatterers helps correctly find new and unique points of damage for each parameter used. The results were validated using reference Grading and crowd-sourced maps. The results illustrate that the proposed approach can be used for detecting and producing informative maps on infrastructural damage detection in urban areas.

Automotive radars play a very important role in reduction of traffic accidents and casualties by making the vehicle fully self aware of its surroundings. For the vehicle to be fully self aware multiple sensors or radars have to placed in close proximity of each other on the body of the automobile. This will lead to interference between the radars which results in sub optimal performance of the radar system. Currently most of interference mitigation techniques employ various methods of signal coding algorithms, by assigning each radar a different code or modifying beamforming and angle of arrival algorithms to suppress the interference. Most of theses technique use and exploit the properties of the transmitted signal (signal processing techniques) for mitigating the interference. In this thesis the avenue of antenna array design for mitigation of interference is explored. By modifying the radiation pattern of the array, the antenna array will act as spatial filter against the incoming interference signals. In this thesis two techniques for designing the antenna array with improved radiation pattern are proposed. The design techniques are used to optimize the MIMO virtual array of the radar for low sidelobes and increased aperture, and the optimized virtual array is then factorized to obtain the Tx and Rx arrays. 2 virtual arrays of 12 elements and 24 elements are optimized using the design techniques. The performance of the various designs is ranked based on the output SINR of an adaptive beamformer. The simulations results show that the optimized MIMO arrays result in an improvement of up to 3.5dB in SINR in comparison to the currently used topologies. Finally, a prototype of the improved MIMO antenna system is modeled in CST MS design environment, and the superior performance of the designed array topologies over the existing array topologies is verified.

This report discusses one part of a project consisting of three parts. The goal of the project is to design a battery less sensor powered by RF energy fields present in an office environment. The sensor is able to measure the temperature and is able to communicate wirelessly using a low power communication protocol. In this project the use case of a building is explored. This thesis treats the RF energy harvesting part, which scavenges the RF power out of the ether and delivers it to the rectifier to convert and store the energy. First the spectrum has been analyzed to determine the most suitable frequency band. An antenna has been designed to harvest as much power at the chosen frequency. To make the power transfer from the antenna to the rectifier as efficient as possible, a matching circuit for matching the impedances is implemented. In this report, a Planar Inverted-F Antenna is designed to meet all the given requirements.

Nowadays, indoor ranging and localization have become necessary in daily life. Due to the multi-path propagation and noise in the indoor environment, phase domain ranging method using multi-frequency has been proposed which achieves accurate estimation of indoor target. However, as the indoor communication is usually carried on Bluetooth Low Energy (BLE) or Zigbee, high efficiency is indispensable in the face of limited bandwidth and measuring time. Thus, in this thesis, we aim to reduce the number of frequencies used in the ranging while keeping an acceptable estimation accuracy. We at first build the signal model and study the ambiguity range of the problem. Then based on the estimation theory and the concept of compressive sensing (CS) theory, we take the Cramer Rao Lower Bound (CRLB) and the matrix coherence as the criteria and select the optimal subset on given tone set. To test the performance of selected subset, we utilize gridless reconstruction algorithms, noiseless global matched filter (NL-GMF) and atomic norm minimization (ANM), to estimate target distance with the subset in both simulated data and real data and provide the mean square error (MSE), the estimation probability and the successful estimation probability in various estimation conditions.

In the hospital environment, the variations in respiratory information (RI) are typically used for indicating the health condition of the patients. With RI known, corresponding parameters in both time domain (respiratory rate) and frequency domain (respiratory power) can be extracted to indicate the health condition. However, the monitoring technologies available in the clinical environment commonly work obtrusively, which can result in discomfort for patients during a long-term monitoring. Meanwhile, these technologies also possess
high false alarm rates, especially during movement of the subjects. Thus, designing an unobtrusive equipment suitable for moving patients that could collect as well as the algorithm to analyze such signals to provide
an accurate and robust estimate of respiratory information obviously becomes very interesting.
The base signals of interest for estimating respiratory information are two commonly used bio-medical signals, Impedance pneumography (IP) and electrocardiography (ECG). Impedance pneumography is a commonly
used method for respiratory rate monitoring. Electrocardiography is a measurement of the electrical activity of heart and respiration modulates the ECG in both time domain and frequency domain so that it is feasible to estimate the RI also from ECG. These two signals can be utilized with the same measurement electrodes attached to the surface of the body from the same device which provides a potential wearable method for real-time RI monitoring.
This thesis investigates the feasibility of using these two unobtrusive bio-medical signals, IP and ECG, to monitor respiratory rate (RR) in home environments especially during various movements. Algorithm development focused on creating accurate and robust respiratory rate estimates both from impedance pneumography and ECG-derived methods based on the modulation of respiration. Furthermore, the RI estimate from individual signals will be further fused by the way of information fusion and investigate the feasibility of giving more accurate respiratory information than that obtained using one signal alone. Finally, we also
investigate the feasibility of using acceleration recordings (Acc) to attenuate the motion artefact distorting the underlying signals.
Signal quality indicators (SQI) and respiration quality indicators (RQI) together play a key role in providing a more accurate RR estimate. Performance of all methods is evaluated against spirometer recordings, as a golden reference, based on a set of typical statisticalmetrics.
The main results are that IP signal can provide a more accurate RR estimate compared with the ECG signal. Furthermore, the derived signal quality indicators, SQI and RQI, could distinguish data of good quality from bad quality and the RR smoothing process could improve the result both of IP and ECG-derived methods
with the help of signal quality indicator. Another main finding is that the correlation between the motion artefacts and Acc recordings is not high enough, resulting in that Acc is not a proper reference signal for motion artefacts attenuation. Finally, it’s also found that information fusion methods could improve the overall estimate from multiple signal sources.
For the future work, a better reference signal for motion artefacts attenuation should be explored. Furthermore, more respiratory information, like tidal volume, should also be explored.

Compressive Sensing (CS) provides a new paradigm in data acquisition and signal processing based on the assumption of sparsity and the incoherence of the received signal. Based on that concept, many radar front-end architectures have been studied with the implementation of CS. In these architectures, less data are collected but the radar scene can be recovered through CS with high probability. All these CS front-ends have always been stated as less complicated but never evaluated. The main motivation of this thesis is to find aspects of the complexity and performance which can be used for the characterization of these front-ends like the gain in signal-to-noise ratio (SNR), number of components, power consumption etc. In this thesis, we investigate three radar front-end architectures in CS. The first two are the Multi-coset (MC) Sampling and Analog-to-Information Converter (AIC) which are widely suggested for telecommunications and radar systems. The fourth front-end is novel as it contains metamaterial surface antenna elements. The performance and the complexity of each architecture are evaluated. The performance is compared with respect to the conventional reference case of a uniform linear array of antennas.