This thesis addresses the design and optimization of sparse non-uniform optical phased arrays (OPAs) for advanced automotive LiDAR systems. As autonomous driving technologies advance, the demand for high-resolution, reliable, and compact LiDAR systems has become increasingly critical. Traditional uniform OPAs, while effective, face limitations regarding power consumption. This work introduces an innovative approach to designing sparse non-uniform OPAs that achieve desired performance metrics essential for automotive applications, including beamwidth, field of view, and sidelobe levels, while minimizing element count and, consequently, energy consumption.

Through mathematical modelling and simulation, we formulate the problem of sparse OPA design as an optimization problem, leveraging techniques from compressive sensing to identify the most efficient element arrangements. We propose using the sparse array synthesis method to formulate the sparse OPA design problem, utilizing algorithms such as LASSO, thresholding, and iterative reweighted l1-norm minimization to achieve optimal sparse configurations. Our results demonstrate substantial improvements in effectiveness, offering a practical solution to the constraints posed by current LiDAR systems. This thesis contributes to the field by providing a comprehensive framework for the design of sparse non-uniform OPAs, highlighting the trade-offs and benefits of various design strategies. The findings advance our understanding of OPA design principles.

Radar technology has evolved into a versatile and robust tool for critical air traffic control, meteorology, surveillance, and defence applications. In surveillance radar, the need for continuous monitoring of large areas, often cluttered by ground or sea reflections, presents significant challenges for multitarget detection. This clutter can obscure true targets, complicating detection in environments where standard radar noise assumptions fall short.

This thesis introduces a novel implementation based on the relevance vector machine (RVM) to address the complexities of multitarget detection in cluttered environments. Unlike conventional approaches that assume white Gaussian noise, the proposed method jointly estimates a clutter covariance matrix, allowing it to adapt to the estimated clutter model over subsequent iterations. Performance evaluations using simulated data in one-dimensional (range or angle) and two-dimensional (range-angle) settings demonstrate that the framework achieves accurate AR parameter estimation. Results indicate a marked improvement in reducing false and missed detections compared to the white-noise-based model. Notably, the framework performs multitarget detection without prior knowledge of target locations and the need for guard cells, underscoring its adaptability to real-world scenarios.

Ambiguities are an often encountered nuisance in signal processing and are the source of some of the fundamental trade-offs encountered in radar systems. The goal of this thesis is to extract unambiguous information about targets by combining a limited amount of measurements on a video integration level. A novel framework is proposed to reach this goal. At the heart of the framework lives a relevance vector machine which is extended to process the ambiguities on a video integration level and to work off-grid. The relevance vector machine is then extended to become the ambiguity aware relevance vector machine. This extension is either performed by a frequentist test or by estimating a posterior distribution. The frequentist test is used to test whether we can statistically significantly discern the returned output from ambiguities. The posterior is estimated according to Bayes’ theorem and thus allows for the incorporation of prior information. In this thesis, the framework is specifically applied to Doppler processing of a pulse-Doppler radar system. Compared to existing methods for estimating unambiguous Doppler velocity in a multi-target environment, the framework provides a general increase in performance, allows for the incorporation of prior information, and is able to give a measure of confidence in the estimates. A simulation study is set up to show the performance increase. This simulation study also highlights the utility of incorporating prior information and the quantification of uncertainty.

Automatic Dependent Surveillance Broadcast (ADS-B) allows aircraft to broadcast their own position, speed, altitude, and other information to ground stations and other nearby aircraft. This information is then used by air traffic control for situational awareness, and collision avoidance. ADS-B spoofing is possible due to the lack of authentication and encryption in the ADS-B protocol. This can result in incorrect decision-making and potential safety hazards. Validation of the location of the ADS-B message is required for Luchtverkeersleiding Nederland (LVNL) such that it can maintain separation minima between civil aircrafts, whilst using ADS-B operationally.
Analysis of possible approaches for ADS-B validation has resulted a multilateration (MLAT) based approach. Time of Arrival (TOA) measurements of the ADS-B messages are used to validate the location. For validation, at least two Ground Stations (GS) are required instead of the four GSs required for a MLAT track, allowing for ADS-B validation in a larger area than it is currently used for in a tracking application. If an ADS-B message is considered validated, its content can be used by ATC. Therefore MLAT based validation results in an increased surveillance coverage. Validation is achieved in two steps, first a tracker is used to compute the state of the target using the TOA measurements, secondly this state is compared to the ADS-B location using a likelihood ratio test.
Tracking is done using a Sequential Importance Resampling (SIR) Particle Filter (PF). Classical PF issues as the degeneracy problem and sample impoverishment problem are mitigated by using a novel sampling method that samples directly from the measurement at the initialization of the SIR filter. Without this novel method a traditional SIR filter (where the proposal density is uniformly distributed) requires roughly a million particles to converge on the location of the target. Below this amount of particles the traditional SIR filter fails. The proposed SIR filter can converge on the location of the target using only 1000 particles.
To provide LVNL options and insights, three different likelihood ratio tests are proposed, namely the Minimum Bayes Risk, The Neyman-Pearson and the Minimax Hypothesis test.
Performance of the algorithm is investigated using a case study where data from LVNL’s Surveillance Data North Sea (SDNS) MLAT system is used. Results have found that each test is capable of correct ADS-B validation. The limiting factor in the validation algorithm is the quality of the state estimate. At lower altitudes (<FL20) state estimation can fail and therefore also the hypothesis test. Above this altitude, spoofed targets can be detected if the distance between the spoofing transmitter and the location inside the spoofed message is roughly 1000 to 2000 meters depending on the hypothesis test used. Horizontally, this falls within LVNL’s separation minima, vertically, this falls outside the separation minima.

The main purpose of a radar is to detect, recognize, and track objects of interest. When it is known that only a single target is present, the matched filter is proven to be optimal detector. However, in practice, a radar scene often consists of multiple targets. For example, in air surveillance and monitoring applications, multiple aircrafts might be in the airspace. When multiple targets are to be detected the matched filter is not guaranteed to give the best results. This can happen when a strong reflector masks the signals reflected from weak reflectors, thereby resulting in missed detections. Furthermore, when the sensor resolution is low, targets that are spaced closely together may only result in a single target actually being detected. This research explores how the Relevance Vector Machine (RVM) framework may be used to achieve a better multi-target detector than the commonly used basic matched filter approach. RVM was selected to resolve the multi-target detection problem as it estimates the target locations iteratively. In this research it was shown how the RVM framework can be used to model the fluctuation of swerling I/II targets. Additionally, the RVM algorithm was modified to incorporate a notion of statistical thresholding. Simulations show that using RVM the false alarm rate can be reduced and target locations can be more accurately recovered compared to other existing methods in case of multiple swerling I/II fluctuating targets. Furthermore, the proposed approach is shown to have a much lower convergence time compared to a similar expectation-maximization based method, namely Enhanced Sparse Bayesian Learning.

Advancements in radar technology such as phased array antennas, digital beamforming, and adaptable waveform generation have led to the flexibility in controlling radar resources such as scan time, beamwidth and bandwidth during a radar mission. This new flexibility has led to a new research topic, radar resource management. Radar resource management involves the allocation of radar resources in order to achieve the highest performance in a radar mission. This thesis focuses on a specific scenario where a radar is tasked with deciding multiple object presence decisions located at multiple scan directions. The resource considered is the scan time allocated to each scan direction. To optimally allocate the scan time over the scan directions, a cost function is formulated, where the expected performance of an individual decision of an object being present or absent is formulated as the expected Bayes risk. The expected performance of all individual decisions in the same scan direction, are summed to obtain an expected task performance. The global performance at the system level is formulated using two approaches. The Sum approach formulates a cost function at the system level as the sum of the expected cost of each task. The Max approach formulates a cost function that minimizes the maximum of all expected task costs. Simulations have been performed to demonstrate the flexibility of the Sum and Max approach to adapt the scan time allocation based on different scenarios, including multiple object presence decisions per scan direction, sequential measurements, and a birth-death process regarding the presence of an object over time. Simulations demonstrate that using the Sum and Max approaches for the allocation of scan time results in an improved performance compared to the uniform distribution of the scan time resource over all scan directions.

The accurate estimation of the pose, i.e. position and heading, of a vehicle while driving is of high importance in autonomous driving applications. Right now the main tool to estimate the location of a vehicle is its GPS sensor. However, GPS data is known to be of very low accuracy, especially in urban environments, and is thus not ideal for this application. In this report, the research into possible improvements to an existing vehicle pose estimation technique are presented, with the aim of making it applicable for automotive radar data. The existing technique is a scan-­matching technique known as the Normal Distributions Transform (NDT), which was originally designed for LIDAR measurements. By adapting the technique to accommodate radar measurements some of the drawbacks of LiDAR, such as the high cost and poor performance in certain weather conditions, can be overcome. Some of the main disadvantages of using radar as compared to LiDAR, e.g. lower resolutions, are addressed in the presented techniques. The lower resolution results in significant spreading of the target response, this is especially prominent in the azimuth domain for a standard 3 Tx × 4 Rx MIMO automotive radar system. By addressing the scan­matching problem in the polar domain, this spreading of the target response is better captured in the distribution used to perform the scan­-matching. Further, the implementation in polar coordinates allows for incorporation of the Doppler measurements, which contain knowledge about the angles of arrival of targets
and are generally measured at a much higher resolution than the angular measurements themselves. Moreover, the use of radar measurements introduces the availability of knowledge about the radar cross­-section of individual targets, this can be used to reduce the influence of false alarms. The incorporation of Doppler additionally allows the exploitation of the relation between the Doppler measurements and the angle of arrival to estimate a bias in the angle measurements, this can be used for sensor calibration while driving. The influence of the presented improvements to the NDT are examined through simulations and experiments. These results show significant reduction in the estimation errors. The sensor bias estimation technique also proves to provide extremely accurate estimates. Finally, a small extension is worked out to perform trajectory estimation using the individual poses by means of the Extended Kalman Filter.

With modern multi-function radars becoming more flexible, handling the limited amount of resources of these radars becomes increasingly important. In this thesis the radar resource management (RRM) problem in a multi-target tracking scenario is considered. Partially observable Markov decision processes (POMDPs) are used to describe each tracking task. By comparing the future effect of radar actions using model predictive control (MPC), the POMDPs are solved in a non-myopic way. The model predictive control problem can be decoupled into sub-problems using Lagrangian Relaxation to reduce the computational complexity of the solution method. An algorithm based on golden section search is employed to find the Lagrange multiplier. An interacting multiple model filter is used to allow the method to be effective in RRM problems involving the tracking of targets performing a broad number of maneuvers.
The novel approach is compared to an existing solution method based on policy rollout and Monte Carlo sampling. Through simulations of dynamic multi-target tracking scenarios in which the cost and computational complexity of different approaches are compared, it was shown that the computational complexity is greatly reduced while the resulting resource allocation results remain similar.

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.

The detection of unusual behavior plays a crucial role in the prevention of illegal and harmful activities such as smuggling, piracy, arms trading, human trafficking and illegal immigration. Also for military applications, it is useful to detect anomalous behavior to provide an alert for potential threats, especially with the more recent widespread use of drones for warfare and terrorist activities. In order to provide a solution for these emerging needs, in this work, a novel method for target dynamic behavior classification by analyzing trajectories using data gathered from multiple heterogeneous sensors is presented. The aim is to develop a context-free (i.e., sensor indifferent) method to robustly classify a selected set of anomalous trajectories and present those results to a human operator in remote sensing applications. In order to track maneuvering targets, it is typically required to use multiple (dynamic) model approach to make dynamic state inferences accurately. In practice, system parameters are typically tuned manually. The approach taken here is to estimate the parameters of a set of these so-called hybrid systems, also known as jump-Markov systems (JMS). This task will be carried out using a maximum likelihood (ML) approach, via the expectation-maximization (EM) algorithm. To handle highly nonlinear measurement models, such as those typically present in remote sensing applications, sequential Monte Carlo (SMC) or particle filters will be used to make accurate state estimations. These methods are also capable of dealing with non-Gaussian measurement or process noise. The central question explored in this work is if jump-Markov systems are a suitable class of models for trajectory classification by learning their parameters from multi-sensor measurement data.

According to literature, MIMO radars often use orthogonal waveforms on their different channels to achieve a wide angular beam and so-called colored transmission. The generation of orthogonal signals is very difficult and true orthogonality cannot be achieved in practice. The Circulating Codes provide a simplified alternative to orthogonal signals, by transmitting the same waveform, but slightly shifted in time from channel to channel. By applying digital beamforming on transmit through signal processing on receive, a performance similar to orthogonal signals can be achieved. The Circulating Codes can also be used with a spatial code along the antenna elements, to improve the auto-correlation properties of the signal and thus the range resolution of the radar. These codes are called Hybrid or Delft Codes.

In this thesis, the Circulating Codes, as well as Hybrid Codes are revisited and analyzed again. An implementation for simulations has been created in Matlab to examine their behavior in more detail. To improve the range resolution of the Hybrid Codes, this thesis proposes the use of so-called Golay pairs as spatial codes. Each Golay pair consists of two codes, which have the property that the sum of their auto-correlation functions produces one strong peak and zero sidelobes. Since the application of these codes requires the transmission of two pulses, the phase shift due to the displacement of a moving target in between of the pulses has an impact on the result. This thesis focuses on the mitigation or correction of this phase shift, by the use of different methods and presents two possible solutions which perform well in simulations.

Finally, some of the techniques are applied in practical measurements with an available radar system. The results of these measurements show that the single-pulse techniques of the Circulating and Hybrid Codes perform well with the system, while the multi-pulse techniques with Golay pairs suffer from much higher sidelobes than expected from the simulations. First of all, this shows that the system has certain issues that need to be improved. Secondly, it shows that Hybrid Codes with Golay pairs are sensitive to disturbances and phase shifts. Therefore, very clean signals are required that might be difficult to generate in practice.