Data acquisition is a necessary first step in digital signal processing applications such as radar, wireless communications and array processing. Traditionally, this process is performed by uniformly sampling signals at a frequency above the Nyquist rate and converting the resulting samples into digital numeric values through high-resolution amplitude quantization. While the traditional approach to data acquisition is straightforward and extremely well-proven, it may be either impractical or impossible in many modern applications due to the existing fundamental trade-off between sampling rate, amplitude quantization precision, implementation costs, and usage of physical resources, e.g. bandwidth and power consumption. Motivated by this fact, system designers have recently proposed exploiting sparse and few-bit quantized sampling instead of the traditional way of data acquisition in order to reduce implementation costs and usage of physical resources in such applications. However, before transition from the tradition data ...

In direction finding (DF) applications, there are several factors affecting the estimation accuracy of the direction-of-arrivals (DOA) of unknown source locations. The major distortions in the estimation process are due to the array imperfections, model mismatches and multipath. The array imperfections usually exist in practical applications due to the nonidealities in the antenna array such as mutual coupling (MC) and gain/phase uncertainties. The model mismatches usually occur when the model of the received signal differs from the signal model used in the processing stage of the DF system. Another distortion is due to multipath signals. In the multipath scenario, the antenna array receives the transmitted signal from more than one path with different directions and the array covariance matrix is rank-deficient. In this thesis, three new methods are proposed for the problems in DF applications in the presence of array ...

Radar backscatter from an ocean surface is commonly referred to as sea clutter. Any radar backscatter not due to the scattering from an ocean surface constitutes a potential target. This thesis is concerned with the study of target detection techniques in the presence of high resolution sea clutter. In this dissertation, the high resolution sea clutter is treated as a compound process, where a fast oscillating speckle component is modulated in power by a slowly varying modulating component. While the short term temporal correlations of the clutter are associated with the speckle, the spatial correlations are largely associated with the modulating component. Due to the disparate statistical and correlation properties of the two components, a piecemeal approach is adopted throughout this thesis, whereby the spatial and the temporal correlations of high resolution sea clutter are treated independently. As an extension ...

Direction-of-Arrival (DOA) estimation from data collected at a sensor array in the presence of noise has been a fundamental and long-established research topic of interest in sensor array processing. The application of DOA estimation does not only restrict to radar but also spans multiple additional fields of research, including radio astronomy, biomedical imaging, seismic exploration, wireless communication, among others. Due to the wide applications of DOA estimation, various methods have been developed in the literature to increase the resolution capability, computational efficiency, and robustness of the algorithms. However, a trade-off between the estimation performance and the computational complexity is generally inevitable. This thesis addresses the challenge of developing low-complexity DOA estimators with the ability to resolve closely spaced source signals in the threshold region, i.e., low sample size or low Signal-to-Noise ratio. Motivated by various interpretations of the conventional DOA ...

This thesis deals with the synchronization of one or several replicas of a known signal received in a scenario with multipath propagation and directional interference. A connecting theme along this work is the systematic application of the maximum likelihood (ML) principle together with a signal model in which the spatial signatures are unstructured and the noise term is Gaussian- distributed with an unknown correlation matrix. This last assumption is key in obtaining estimators that are capable of mitigating the disturbing signals that exhibit a certain structure, and this is achieved without resorting to the estimation of the parameters of those signals. On the other hand, the assumption of unstructured spatial signatures is interesting from a practical standpoint and facilitates the estimation problem since the estimates of these signatures can be obtained in closed form. This constitutes a first step towards ...

This work deals with the problem of calculating the Cramer-Rao lower bounds (CRLBs) for bistatic radar channels. To this purpose we exploited the relation between the Ambiguity Function (AF) and the CRLB. The bistatic CRLBs are analyzed and compared to the monostatic counterparts as a function of the bistatic geometric parameters. In the bistatic case both geometry factors and transmitted waveforms play an important role in the shape of the AF, and therefore in the estimation accuracy of the target range and velocity. In particular, the CRLBs depend on the target direction of arrival, the bistatic baseline length, and the distance between the target and the receiver. The CRLBs are then used to select the optimum bistatic channel (or set of channels) for the tracking of a radar target moving along a trajectory in a multistatic scenario and for design ...

Recent advances in computer technology and speech and language processing have made possible that some new ways of person-machine communication and computer assistance to human activities start to appear feasible. Concretely, the interest on the development of new challenging applications in indoor environments equipped with multiple multimodal sensors, also known as smart-rooms, has considerably grown. In general, it is well-known that the quality of speech signals captured by microphones that can be located several meters away from the speakers is severely distorted by acoustic noise and room reverberation. In the context of the development of hands-free speech applications in smart-room environments, the use of obtrusive sensors like close-talking microphones is usually not allowed, and consequently, speech technologies must operate on the basis of distant-talking recordings. In such conditions, speech technologies that usually perform reasonably well in free of noise and ...

Array processing is an area of study devoted to processing the signals received from an antenna array and extracting information of interest. It has played an important role in widespread applications like radar, sonar, and wireless communications. Numerous adaptive array processing algorithms have been reported in the literature in the last several decades. These algorithms, in a general view, exhibit a trade-off between performance and required computational complexity. In this thesis, we focus on the development of array processing algorithms in the application of beamforming and direction of arrival (DOA) estimation. In the beamformer design, we employ the constrained minimum variance (CMV) and the constrained constant modulus (CCM) criteria to propose full-rank and reduced-rank adaptive algorithms. Specifically, for the full-rank algorithms, we present two low-complexity adaptive step size mechanisms with the CCM criterion for the step size adaptation of the ...

The advent of multi-microphone setups on a plethora of commercial devices in recent years has generated a newfound interest in the development of robust microphone array signal processing methods. These methods are generally used to either estimate parameters associated with acoustic scene or to extract signal(s) of interest. In most practical scenarios, the sources are located in the far-field of a microphone array where the main spatial information of interest is the direction-of-arrival (DOA) of the plane waves originating from the source positions. The focus of this thesis is to incorporate robustness against either lack of or imperfect/erroneous information regarding the DOAs of the sound sources within a microphone array signal processing framework. The DOAs of sound sources is by itself important information, however, it is most often used as a parameter for a subsequent processing method. One of the ...

In the field of airborne earth observation there is special attention to compact, cost effective, high resolution imaging sensors. Such sensors are foreseen to play an important role in small-scale remote sensing applications, such as the monitoring of dikes, watercourses, or highways. Furthermore, such sensors are of military interest; reconnaissance tasks could be performed with small unmanned aerial vehicles (UAVs), reducing in this way the risk for one's own troops. In order to be operated from small, even unmanned, aircrafts, such systems must consume little power and be small enough to fulfill the usually strict payload requirements. Moreover, to be of interest for the civil market, cost effectiveness is mandatory. Frequency Modulated Continuous Wave (FMCW) radar systems are generally compact and relatively cheap to purchase and to exploit. They consume little power and, due to the fact that they are ...

The main objective of the thesis is the development of efficient multipath mitigation techniques for navigation systems. By efficient mitigation we refer to the use of asymptotic efficient estimators, and also to the minimization of their computational burden. In this thesis, the efficient estimators are derived from the Maximum Likelihood Principle in several important scenarios. The computational burden is reduced in two ways. One is through data compression techniques that yield receiver implementations of small complexity and small data sizes. The other consists of the efficient implementation of Newton-type methods for the computation of the Maximum Likelihood estimators. The first part of the thesis is dedicated to present the fundamentals of synchronization in a navigation receiver, and to the state of the art in multipath mitigation. Afterward, several results concerning the interpolation of a band limited signal in a finite ...

Multisensor systems are a key enabling technology in, e.g., radar, sonar, medical ultrasound, and wireless communications. Using multiple sensors provides spatial selectivity, improves the signal-to-noise ratio, and enables rejecting unwanted interference. Conventional multisensor systems employ a simple array of uniformly spaced sensors with a linear or rectangular geometry. However, a uniform array spanning a large electrical aperture may become prohibitively expensive, as many sensors and costly RF-IF front ends are needed. In contrast, sparse sensor arrays require drastically fewer resources to achieve comparable performance in terms of spatial resolution and the number of identifiable scatterers or sources. This is facilitated by the co-array: a virtual array structure consisting of the pairwise differences or sums of physical sensor positions. Most recent works on co-array-based sparse array design focus exclusively on passive sensing. Active sensing, where sensors transmit signals and observe their ...

This dissertation investigates robust signal processing and machine learning techniques, with the objective of improving the robustness of two applications against various threats, namely Global Navigation Satellite System (GNSS) based positioning and satellite imaging. GNSS technology is widely used in different fields, such as autonomous navigation, asset tracking, or smartphone positioning, while the satellite imaging plays a central role in monitoring, detecting and estimating the intensity of key natural phenomena, such as flooding prediction and earthquake detection. Considering the use of both GNSS positioning and satellite imaging in critical and safety-of-life applications, it is necessary to protect those two technologies from either intentional or unintentional threats. In the real world, the common threats to GNSS technology include multipath propagation and intentional/unintentional interferences. This thesis investigates methods to mitigate the influence of such sources of error, with the final objective of ...

Global Navigation Satellite Systems (GNSS) signals are broadly used for positioning, navigation and timing (PNT) in many different applications and use cases. Although different PNT technologies are available, GNSS is expected to be a key player in the derivation of positioning and timing for many future applications, including those in the context of the Internet of Things (IoT) or autonomous vehicles, since it has the important advantage of being open access and worldwide available. Indeed, GNSS is performing very well in mild propagation conditions, achieving position and time synchronization accuracies down to the cm and ns levels, respectively. Nevertheless, the exploitation of GNSS in harsh propagation conditions typical of urban and indoor scenarios is very challenging, resulting in position errors of up to tens or even hundreds of meters, and timing accuracies of hundreds of ns. This thesis deals with ...

This thesis comprises two parts. The first part deals with single carrier and multiple-carrier based frequency estimation. The second part is concerned with the application of ultrasound using the proposed estimators and introduces a novel efficient implementation of a subspace tracking technique. In the first part, the problem of single frequency estimation is initially examined, and a hybrid single tone estimator is proposed, comprising both coarse and refined estimates. The coarse estimate of the unknown frequency is obtained using the unweighted linear prediction method, and is used to remove the frequency dependence of the signal-to-noise ratio (SNR) threshold. The SNR threshold is then further reduced via a combination of using an averaging filter and an outlier removal scheme. Finally, a refined frequency estimate is formed using a weighted phase average technique. The hybrid estimator outperforms other recently developed estimators and ...