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.

This thesis explores the dealiasing methods of the Doppler spectrum for the case of a fast-scanning weather radar with the possibility of being used in the development of future weather radars. From the literature, log-periodic sampling which is a type of non-uniform sampling is adapted for Doppler dealiasing. The sampling parameters of log-periodic are optimized to provide superior point Doppler dealiasing performance by orders of magnitude. To extend the dealiasing to weather targets with a wider spectrum, the log-periodic sampling is embedded into a ’Periodic Non-Uniform Non-Coherent Burst (PNU-NCB)’ structure. Additionally, the unique structure of PNU-NCB enables it to be used readily for fast-scanning radars as a multi-burst processing scheme. Furthermore, an Iterative Adaptive Approach (IAA) algorithm is used in combination with PNU-NCB to suppress the effect of noise and enable the estimation of Doppler moments for wider Doppler targets. The final performance of the designed waveform after processing yields the dealiasing and estimation of moments for even extended Doppler targets with extremely good performance. By using, the optimized PNU-NCB coupled with IAA, it is possible to accurately estimate the Doppler moments unambiguously for targets smaller than 0.1 times the uniform sampling Nyquist window. For higher target spectral width, the estimation of the spectral moments is comparable to the performance in the uniform sampling case with the added benefit of resolving ambiguities. Further performance improvements to the performance can be achieved by exploring the future recommendations provided.

A novel new waveform is compared with two other existing waveforms to create a radar network. In this radar network one primary radar transmits a waveform for sensing and communication and one or multiple radars receive these waveforms. The constraint for the radars is that all radars can build up a radar picture and situation awareness. What also means that the performance of the primary radar does not degrade. This new radar type is called cooperative hitchhiker with communications. In this radar network the main task is sensing, therefore additional communication signals are used to increase the performance of the sensing task. The advantages of parallel sensing and communications are reducing interference, dual use of the scarce electromagnetic (EM) spectrum in a congested EM environment and it is possible in a bistatic configuration to detect a bistatic scattering object.
The three waveforms which are compared are phase coded frequency modulated continuous wave (PC FMCW) linear frequency modulated – minimum shift keying (LFM-MSK) and a new waveform time delay between radar bursts (TDBRB). TDBRB is a simple but effective communication method on top of the sensing waveform and can be used with staggered waveforms. Where the first two waveforms make use of binary phase shift keying (BPSK), TDBRB uses a time modulation with the inserted time delay between two successive radar bursts. To distinguish the two radar bursts the first burst has a linear frequency modulated (LFM) upchirp and the successive burst, after the inserted time delay, has an LFM downchirp. PC FMCW and LFM-MSK data are compared with simulations and an experiment of the TDBRB waveform.
The conclusions are that PC FMCW has the highest data rate, followed by LFM-MSK, and TDBRB has the lowest data rate for line-of-sight connections. The first two make use of the information in one pulse, or FMCW chirp. This results in a lower signal-to-noise ratio (SNR) and is therefore only suited for line-of-sight connections. With a matched filter and coherent integration of the TDBRB signals, this waveform is most suited for non-line-of-sight connections and communication over an object-of-interest at the cost of a lower bit rate. TDBRB has a similar performance for the different Swerling cases as the detection probability of a regular radar. The degradation in performance with the TDBRB waveform is only a fraction of the burst time because the time delay is added to an original radar burst. These results make TDBRB most suited for non-line-of-sight communication and for communications with a low signal-to-noise ratio.

Nowadays, accurate vehicle classification plays a critical role in Advanced Driver Assistant Systems (ADASs), autonomous driving systems, and traffic monitoring systems. The benefits of utilizing additional polarimetric information in road target classification have been revealed in the literature. This thesis investigates the polarimetric characteristics of multi-class vehicles and explores new features contributing to vehicle classification, using a labeled street-way database extracted from the PARSAX S-band polarimetric Frequency Modulated Continuous Wave (FMCW) radar. The vehicle classes involved in this thesis are sedan, sedan with extended luggage bin, mini-van, small truck, and large truck.

Three calibration algorithms are proposed and validated for calibrating the labeled street-way database to ensure feature quality. The first channel calibration algorithm removes the channel-specific amplification factors and biases due to the non-ideal and non-identical electronic devices in the four polarimetric channels of the PARSAX radar. The second phase compensation algorithm compensates the phase difference between the H- and V-polarized channels, which is caused by the time shift between the transmitted H- and V-polarized signals. The last antenna pattern compensation algorithm resolves the power degradation in the measurements due to the PARSAX radar beam width limitation.

Based on the calibrated labeled street-way database, multiple polarimetric features are extracted from the Polarization Scattering Matrices (PSMs), coherency and covariance matrices using the eigenvalues/eigenvectors decomposition methods. These matrices represent either the central bodies or the whole bodies of the vehicles. In each case, the eigenvalues and eigenvectors are analyzed to indicate the vehicles' reflection amplitudes/power and polarization basis. Furthermore, these features are evaluated, and most of the amplitudes/power-based features show great classification capabilities. However, all vehicles have a similar polarization basis, which does not have a contribution to vehicle classification. In addition, the target length and eigenvalues of the covariance matrix of detection cells are also extracted as potential features. The feature evaluation results show that the target length and the first eigenvalue of the covariance matrix of detection cells have classification capabilities, while the second eigenvalue does not contribute to vehicle classification.

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.

There is an increasing demand for a highly accurate weather system on the runway for early detection and warning of severe weather phenomena. Due to the high performances of a phased array radar system in terms of time-resolution and elevation-resolution, an already existing fast-rotating phased array radar, 60 RPM, is studied to find the possibility to implement a second processing chain, dedicated to weather applications. Thus, the research in this project is carried out for signal processing algorithms for beamforming, target mask, and Doppler aliasing correction, meant to reconstruct 3D Doppler maps at high estimation accuracy of precipitation profiles, and determine this radar potential as weather radar.

In order to ensure safety and prevent collisions on road, automotive radars must be fault proof and have to be tested on reliability and performance, which requires proper diagnostic of well-functioning of the radar so that the car may participate to traffic. One way of the diagnostic of well-functioning of the automotive radar is by means of calibration in service stations. In contrast to offline calibration in service stations, one another way of testing the automotive radar on well-functioning would be by means of monitoring the state, or with other words the healthiness, of the radar in real-time. One possible solution to monitor the radar state is to use a massive set of calibration targets in road infrastructure and thereby the problem lies in optimally estimating the state based on the RCS information provided by the radar. As a result, in the first part of this thesis, based on the defined target selection criteria, selection of the most appropriate calibration target among possible candidates is discussed. Using massive set of targets is required to overcome the uncertainty in production and installation accuracy in one-target measurements. However, this method brings randomness which is caused by two error sources being the non-ideal shapes of the calibration targets originating from mass production errors and orientation errors either from installation or maintenance errors. Second part of the thesis investigates how this randomness affects the self-diagnostics performance of automotive radar. Thereby, the model of target orientation and RCS loss due to orientation errors, and, the model of target RCS and its RCS loss due to mass production errors are developed. For both error sources, the statistical characteristics of the loss factors are determined by means of corresponding probability distribution functions which are derived analytically. In case of orientation errors, analytical results are validated by Monte-Carlo simulations as well as Kullback-Leibler Divergence. In case of mass production errors, analytical results are validated by Monte-Carlo simulations only. Together with the results obtained in the first part, results of the statistical characteristics of the loss factor due to non-orthogonality help to find a balance between the size, quality and number of targets to be deployed in a certain range in a given road configuration. To finalize the project, the measurement model is determined according to which the approach for self-diagnostics is developed under certain assumptions and considering a set of measurements of the same realization of a single target only. By the developed approaches, the relation of statistical parameters on self-diagnostics performance is determined by means of three different estimation methods and the results are validated by Monte-Carlo simulations.

Nowadays, many practical radar applications require an automatic interpretation of the received data, including data processing algorithms and target classification. The exploitation of additional polarimetric information is a very promising concept to improve the performance of automotive target classification. In this thesis work, we aim to identify target features that can be used for the classification and definition of sub-classes of moving automotive vehicles driving on a highway. This analysis is based on a multi-dimensional polarimetric feature database, created from real observations from a fully polarimetric-Doppler S-band FMCW radar (PARSAX). The polarimetric information of the vehicles is extracted while tracking the targets in a multi-target environment in the range-Doppler domain. Therefore, a multi-target tracking algorithm, based on an OS-CFAR detector, polarimetric data fusion algorithm, and a classical Kalman filter, is used. In order to cope with Doppler ambiguity, a novel MHT-based approach has been introduced.

The feature extraction analysis shows that the polarimetric features of the observed targets provide well-defined reliable statistical relations between physically related features, but that blind classification based on our target feature database does not provide new insights that are useful for classification. Reliable clusters that are useful to describe the polarimetric signatures of the targets have not been found, except the polarimetric correlation coefficients, which, unfortunately, despite their physical clear sense, were not supported by the other analyzed features. Nevertheless, from similar feature analysis, it has been shown that the features originating from the incoherent polarimetric H/A/α-decomposition form compact and well-separated clusters corresponding to target scattering and clutter scattering. Therefore, it can be concluded that these features can be used to accurately distinguish moving vehicles from static clutter.

In many countries the number of wind turbines is growing rapidly as a response to the increasing demandfor renewable energy.Modern wind turbines are large structures, many reach more than 150 meters above theground. Clusters of densely spaced wind turbines, so called wind farms, are being built both on- and offshore. Wind farm installations relatively near to radar systems generate clutter returns that usually affect the normal operation of these radars. Interference caused by wind turbines is more severe for many radar systems than interference caused by stationary objects such as masts or towers. This is due to the rotating blades of the wind turbines. Many Doppler radars use a filter that removes echoes originating from objects with no or little radial velocity. However, these filters do not work for rotating objects such as the rotating blades of wind turbines. Wind turbines located around the line of sight of Doppler radars can cause clutter, blockage, and erroneous velocity measurements, affecting the performance of both military and civilian radar systems. As a result, the unwanted radar return from wind farms, known as Wind Turbine Clutter (WTC), is considered to be dynamic clutter due to the nonzero Doppler return created by rotating wind turbine blades. Nowadays numerous radar systems are developed in order to exploit the diverse information obtained through transmission of waves with different polarizations. This technique is widely known as polarimetry. Many targets of interest exhibit Radar Cross Sections which vary with different transmitted and received polarizations. Wind Turbines also experiences this variability. In this thesis we propose a method to optimal detect the presence of WTC with the use of radar polarimetry. Since the crucial part of this interference comes from the blades rotation, we initially propose a method to estimate the angular velocity of these blades. The estimation of this parameter is derived with the use of proper combination of maximum likelihood estimation theory and radar polarimetry. As there is absence of Micro-Doppler when the radar beam axis and rotation coincide, a separate estimator for this case is pro-posed. In the final part of this thesis, we present a detection approach based on the same signal model used for angular velocity estimation. Again we define a detection rule for the case when radar beam axis and rotation axis coincide and one when they do not. Although at some extent the used model for the second case is valid for low frequencies (f<1 GHz), both estimator and detector derivations can be further applied for higher frequencies signal models. All these mathematical derivations are accompanied with proper simulations.

Automotive radar has an advantage over other sensors in that it is better at operating in bad weather conditions. To see the extent of the effect that adverse weather conditions might have on the statistics of the data a statistical analysis was performed on real measurement data. During heavy rain there is a shift that can be observed in the Radar Cross Section (RCS) of the target. The average RCS of the target increases slightly when it is raining. In (Multiple Input Multiple Output) MIMO radar it is important to calibrate the radar system as there can be both amplitude and phase distortions between the channels that can give unexpected results. These are usually estimated in a predetermined setting for known targets. However instead it might be feasible to estimate this from objects of opportunities that are regularly appearing in the radar field of view.
To tackle this problem a method is used that tries to estimate these calibration coefficients from measurement data. The method needs to know the angle at which the target is located, however the range of the target can remain unknown. It uses the ideal steering vector and one of the antenna elements as a reference element. The method can recreate the phase errors very well, but relies on the reference element for the amplitude estimation. Therefore the performance is based on what element is chosen as a reference. To choose the right reference element some pre-processing is done. Then the estimation of the calibration coefficients was implemented in a Simultaneous Localization And Mapping (SLAM) framework. This was solved by using an Extended Kalman Filter (EKF). The EKF is a nonlinear form of the normal Kalman filter that will be used to make an estimate for both the location of the radar, the location of the objects of opportunity and the estimation of the calibration coefficients based of these landmarks at the same time. The resulting algorithm proves that it is feasible to calibrate the radar while driving in this way.

In this thesis, we propose, a Robust data extraction algorithm capable of extracting reliable target features of multiple moving targets of different classes over all channels of an S-Band Doppler Polarimetric Radar PARSAX. The proposed algorithm is capable of generating a time series data by tracking, clusters of detections - representing extended targets using a multi-target tracker modified to track on sequential frames of Range Doppler Maps. The targets considered in this study are Automobiles of different classes (4 wheel drive and above). A performance analysis of the algorithm, for data extraction possibility with respect to target density, has been presented. In addition, the possibility to use the extracted features for Radar Classification has been investigated.

In the recent years, there has been an increase in the popularity of renewable energy sources. One such being the wind energy. With the development in the field of wind energy, the structure of the wind turbines (WT) has also increased. The huge wind turbine structure (WTS) and the wind farms, both offshore and onshore, has a strong impact on the radar signal communication. The wind turbine clutters (WTC), which is a result of strong Electromagnetic Interference (EMI), has affected the existing radar system such as air-traffic control, weather radars, etc. Thus, to mitigate the wind turbine clutters (WTC), there is a need to understand the scattering properties of the wind turbine structure (WTS).The thesis proposes a development of a radar model to study the radar signal scattering on the wind turbine blades (WTB). The proposed model is developed in a simplified approach to studying the time domain simulationof signal scattered on the WTS. The blades of the model are represented as linear wire structures, which are designed using thin wire approximation. It is implemented for arbitrary orientation of the WT with full 3-D polarimetry observation, which can be applied to different azimuth and aspect angle. The signal scattering from theWTB will be developed for mono-static scattering and bi-static scattering case. The scattering matrix was derived for the cases and the results of the different polarizations are analyzed.The models developed until the date has been focused on the high-frequency range (> 1GHz). To bring noveltyto the model, it will be analyzed for very-high (VH) and ultra-high (UH)frequencies (50, 280, and 600 MHz).Another feature of the model proposed includes the capability to simulate the range profile with arbitrary resolution. To implement this, pulse compression and stepped frequency waveform is used to attain high resolution with narrow-bandwidth. The results of the model will be validated using measurements data provided by the Agentschap Telecom, the Netherlands. The model is further extended to study the contribution of the mast in the signal scattering characteristics of theWTS.