The challenge of avoiding aliasing in the Doppler spectrum for precipitation is addressed. A novel integrative signal processing approach has been proposed to address the research gaps from various disciplines. The proposed approach consists of several steps. First, an aperiodic way of sampling the echoes (aperiodic sampling refers to aperiodic pulse train in the context of radar echoes in slow time) has been proposed by which the maximum unambiguous Doppler frequency (velocity) is enhanced. Second, the Doppler spectrum moment estimation is performed with the help of a parametric form of its covariance. The performance of the moment estimation is assessed by the bias and the variance in the estimated counterparts. The theoretical variance for the parameter estimation is also derived. An aperiodic pulse train design recommendation has been proposed for adequately and unambiguously estimating the Doppler moments for one extended target (like precipitation). Finally, a spectrum reconstruction technique is implemented after the moment estimation on simulated radar echo samples for a realistic precipitation-like event. The comparison with the other approaches proves its superiority for parameter estimation and Doppler spectrum reconstruction.

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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

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.

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

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 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.

We consider a target selection problem for multitarget tracking in a multifunction radar network from a gametheoretic perspective. The problem is formulated as a noncooperative game. The radars are considered to be players in this game with utilities modeled using a proper tracking accuracy criterion and their strategies are the observed targets whose number is known. Initially, for the problem of coordination, the Nash equilibria are characterized and, in order to find equilibria points, a distributed algorithm based on the bestresponse dynamics is proposed. Afterwards, the analysis is extended to the case of partial target observability and radar connectivity and heterogeneous interests among radars. The solution concept of correlated equilibria is employed and a distributed algorithm based on the regret-matching is proposed. The proposed algorithms are shown to perform well compared to the centralized approach of significantly higher complexity

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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.

In this paper, a track selection problem for multi-target tracking in a multifunction radar network is studied using the concepts from game theory. The problem is formulated as a non-cooperative game, and specifically as an anti-coordination game, where each player aims to differ from what other players do. The players' utilities are modeled using a proper tracking accuracy criterion and, under different assumptions on the structure of these utilities, the corresponding Nash equilibria are characterized. To find an equilibrium, a distributed algorithm based on the best-response dynamics is proposed. Finally, computer simulations are carried out to verify the effectiveness of the proposed algorithm in a multi-target tracking scenario.@en