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.

Clock synchronization among the nodes of a wireless acoustic sensor network (WASN) is a significant issue that affects the performance of multi-channel noise reduction schemes. Since independent sensors are utilized, each accompanied by its internal clock, clock offsets are inevitable, even if the mismatch in the sampling frequencies is negligible. In this thesis, clock offsets are mathematically modeled and the problem of multi-channel linear filtering for speech enhancement is addressed through signal subspace methods. For this purpose, the generalized eigenvalue decomposition (GEVD) of the cross-power spectral density (CPSD) matrices of the noise and target speech processes is capitalized. Beamformers based on this technique are proved to be invariant to sensor clock offsets when used in a blind manner, exploiting only network measurements. This result is confirmed through experiments in a simulated environment.

The Radar Resource Management (RRM) problem in a multi-sensor multi-target scenario is considered. The problem is defined as a constrained optimization problem in which the predicted error covariance is minimized subject to resource budget constraints. By applying Lagrangian Relaxation (LR) the problem is decoupled into multiple sub-optimization problems. The problem is modeled according to a Partially Observable Markov Decision Process (POMDP). Using a stochastic optimization framework called policy rollout, the POMDP is solved non-myopically by looking ahead into the expected future. Two novel implementations, namely a centralized and distributed implementation are presented as viable approaches for solving this problem for a multi-sensor case. The centralized implementation, defined as the approximately optimal solution, utilizes a global policy per task. As such, the policy rollout for a single target needs to explore the actions of multiple sensors. The distributed implementation is considered as a practical alternative to improve on the computational complexity of the policy rollout of the centralized implementation. Now per sensor and per task a policy rollout is computed. To maintain a similar performance as the centralized implementation, at the beginning of each policy rollout the last known actions of the other sensors are shared. An additional third independent implementation is considered. The independent implementation uses no communication during the optimization process and is considered to be the implementation with the lowest performance with respect to the cost. All implementations have been applied to multiple two-dimensional simulated radar tracking scenarios. A comparison is made between the centralized, distributed and independent implementation based on the average cost and runtime. Results indicate that both the centralized and distributed implementation outperform the independent implementation with respect to the cost by a factor two. Subsequently, the distributed solution converges to similar results as the centralized implementation while requiring significantly less computational resources.

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.

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.

Recent advances in Multi-Function Radar (MFR) systems led to an increase in their degrees of freedom. As a result, modern MFR systems are capable of adjusting many parameters during runtime. An automatic adaptation of the radar system to changing situations, like weather conditions, interference, or target maneuvers, is often mentioned in the context of MFR and is usually called Radar Resource Management (RRM). This thesis aims at developing a generic framework and approximately optimal algorithmic solutions for solving RRM problems. This is achieved by formulating the sensor tasks as Partially Observable Markov Decision Processes (POMDPs). Although the focus is on MFR, the approach is not limited to such sensor systems and has broader applicability. In Chapter 2, a first step is taken by investigating Lagrangian Relaxation (LR) and the subgradient method for optimally distributing the sensor resources to the different tasks in a multi-target tracking scenario. A constrained optimization problem is formulated. Using LR, the constraints can be included in the cost function. In a time-invariant scenario, it is shown that the proposed Optimal Steady-State Budget Balancing (OSB) algorithm will lead to balanced budgets based on track parameters like maneuverability and measurement uncertainty. The time-invariant scenario is a special case of general tracking scenarios, and the presented solution can be seen as the optimal POMDP solution in that case. Since real-world applications quickly lead to time-varying scenarios, it is demonstrated how the approach can be extended to such cases. Finally, the proposed method is compared with other budget assignment strategies. Subsequently, the tracking tasks are explicitly formulated as POMDPs, and the novel Approximately Optimal Dynamic Budget Balancing (AODB) algorithm is proposed in Chapter 3. The algorithm applies a combination of LR and Policy Rollout (PR). PR is a Monte Carlo sampling method for POMDPs to find the expected future cost. Due to its generic architecture, the framework can be applied to different radar or sensor systems and cost functions. In a time-invariant scenario, the algorithm calculates a solution close to the optimal steady-state solution, as presented in Chapter 2. This is shown through simulations of a two-dimensional tracking scenario. Moreover, it is demonstrated how the algorithm dynamically allocates the sensor time budgets to the tasks in a changing environment using a non-myopic fashion. Finally, the algorithm's performance is compared with different resource allocation techniques. Based on the previous results, Chapter 4 conducts a detailed investigation of the computational load of the AODB algorithm. It is shown how the choice of several input parameters influences computational performance. Additionally, Model Predictive Control (MPC) is applied in the same framework as an alternative POMDP solution method. Compared to stochastic optimization methods such as PR, the computational load is dramatically reduced while the resource allocation results are similar. This is shown through simulations of dynamic multi-target tracking scenarios in which the cost and computational load of different approaches are compared. So far, this thesis has used tracking scenarios to demonstrate the validity of the proposed algorithms. Chapter 5 shows how to apply the proposed framework and algorithmic solution to a multi-target joint tracking and classification scenario. It is shown that tracking and classification can be considered in a single task type. Furthermore, it is shown how the task resource allocations can be jointly optimized using a single carefully formulated cost function based on the task threat variance. Multiple two-dimensional radar scenarios demonstrate how sensor resources are allocated depending on the current knowledge of the target position and class. Chapter 6 extends the single-sensor approach shown in the previous chapters to multiple sensors and demonstrates the usefulness of the proposed algorithm in two different multi-sensor multi-target tracking scenarios. The first scenario considers a generic surveillance situation. An approximately optimal approach based on the previously proposed algorithm is formulated assuming a central processor. Subsequently, a distributed implementation is introduced that converges to the same results as the centralized implementation and requires less computational resources. The performance of the proposed approach for both centralized and distributed implementation is demonstrated through dynamic tracking scenarios. The second scenario focuses explicitly on an automotive application. The proposed generic framework and algorithmic solution are used to allocate scarce resources across multiple mobile sensor nodes. A central system manages the nodes' transmission and shares sensing data with other sensor nodes if this improves the overall track accuracy. The proposed method allocates time and frequency resources. Through simulation of a typical traffic situation, the validity of the approach is demonstrated. This thesis shows that the application of the proposed novel generic framework and algorithmic solution increases the performance w.r.t. heuristic solutions. Furthermore, it is demonstrated that the proposed framework allows the user to exchange elements such as cost function or POMDP solution method to adjust it to specific needs. The proposed method can be applied in many different areas involving different types of sensors. Possible applications include automotive scenarios, such as autonomous driving or traffic monitoring, (maritime) surveillance, and air traffic control.@en

Radar-tracking of low-observable targets such as drones suffers from low detection performance. In these type of applications, it is desirable to avoid data thresholding in order to preserve the weak target signal in the raw sensor data. This thesis considers the Multiple Object Tracking (MOT) problem in the context of radar Track-before-Detect (TrBD) processing, where the raw radar data is fed into the filtering process without previous compression into a finite set of detection/plots....@en

Indoor positioning using Bluetooth addressed a great concern. The properties of narrowband usage, low energy consumption and universality on devices attract numerous customers and promote researchers to investigate potential of Bluetooth in indoor positioning field. It has already supported Angle-of-Arrival (AoA) and Angle-of-Departure (AoD) in angle domain for indoor localization. In range domain, using Received Signal Strength (RSS) is practicable but it cannot provide enough resolution. It is needed to develop a technique that can improve resolution for range finding feature. The challenges of localization in indoor environments are mainly from the multipath propagation of signals. In this thesis, a subspace-based super-resolution algorithm for time delay dispersion estimate is developed. Range finding is realized by computing time-of-arrival (ToA) parameter of signal arriving at direct line-of-sight (DLoS). An essential procedure before the developed algorithm is applied is that to correctly separate subspace into signal space and noise space. Techniques for subspace separation are investigated in this thesis. In this thesis, we want to explore the potential of indoor localization using Bluetooth narrowband radios. To start with, a data model according to the property of the conducted measurement data is developed. The conducted measurement data is radio channel measurements based on channel sounding technique. Then the data model is developed as channel impulse response model and multipath signals are indicated by different time delays. Since an accurate covariance matrix of measurement data is required for super-resolution algorithm, smoothing techniques is employed. The smoothing techniques considered are forward smoothing technique and forward-backward smoothing technique. For the purpose of obtaining an accurate subspace separation, two techniques are investigated in this thesis, namely MDL criteria algorithm and the threshold method. in order to investigate the performance and reliability of those two techniques, experiments are taken out using different parameter values. Comparison is made between the results of these two techniques. Afterwards, subspace-based super-resolution algorithm is taken into consideration. In this thesis, the super-resolution algorithm implemented is MUSIC algorithm. The functionality of MUSIC algorithm on narrowband radios measurements is tested and evaluated firstly by simulation experiments, which demonstrates the practicability of applying MUSIC algorithm on narrowband radios measurements. Then experiments are extended to the measurement data that conducted from real indoor environments, for the purpose of indoor localization realization using narrowband radios.

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.

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.

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.

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.

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.