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 ...

Statistical robustness and collaborative inference in a distributed sensor network are two challenging requirements posed on many modern signal processing applications. This dissertation aims at solving these tasks jointly by providing generic algorithms that are applicable to a wide variety of real-world problems. The first part of the thesis is concerned with sequential detection---a branch of detection theory that is focused on decision-making based on as few measurements as possible. After reviewing some fundamental concepts of statistical hypothesis testing, a general formulation of the Consensus+Innovations Sequential Probability Ratio Test for sequential binary hypothesis testing in distributed networks is derived. In a next step, multiple robust versions of the algorithm based on two different robustification paradigms are developed. The functionality of the proposed detectors is verified in simulations, and their performance is examined under different network conditions and outlier concentrations. Subsequently, ...

The level of automation in our society is ever increasing. Technologies like self-driving cars, virtual reality, and fully autonomous robots, which all were unimaginable a few decades ago, are realizable today, and will become standard consumer products in the future. These technologies depend upon autonomous localization and situation awareness where careful processing of sensory data is required. To increase efficiency, robustness and reliability, appropriate models for these data are needed. In this thesis, such models are analyzed within three different application areas, namely (1) magnetic localization, (2) extended target tracking, and (3) autonomous learning from raw pixel information. Magnetic localization is based on one or more magnetometers measuring the induced magnetic field from magnetic objects. In this thesis we present a model for determining the position and the orientation of small magnets with an accuracy of a few millimeters. This ...

The world in which we live is becoming more and more automated, exemplified by the numerous robots, or autonomous vehicles, that operate in air, on land, or in water. These robots perform a wide array of different tasks, ranging from the dangerous, such as underground mining, to the boring, such as vacuum cleaning. In common for all different robots is that they must possess a certain degree of awareness, both of themselves and of the world in which they operate. This thesis considers aspects of two research problems associated with this, more specifically the Simultaneous Localization and Mapping (SLAM) problem and the Multiple Target Tracking (MTT) problem. The SLAM problem consists of having the robot create a map of an environment and simultaneously localize itself in the same map. One way to reduce the effect of small errors that inevitably ...

An adaptive edge-enhanced correlation based robust and real-time visual tracking framework, and two machine vision systems based on the framework are proposed. The visual tracking algorithm can track any object of interest in a video acquired from a stationary or moving camera. It can handle the real-world problems, such as noise, clutter, occlusion, uneven illumination, varying appearance, orientation, scale, and velocity of the maneuvering object, and object fading and obscuration in low contrast video at various zoom levels. The proposed machine vision systems are an active camera tracking system and a vision based system for a UGV (unmanned ground vehicle) to handle a road intersection. The core of the proposed visual tracking framework is an Edge Enhanced Back-propagation neural-network Controlled Fast Normalized Correlation (EE-BCFNC), which makes the object localization stage efficient and robust to noise, object fading, obscuration, and uneven ...

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 ...

Adaptive filtering algorithms are powerful signal processing tools with widespread use in numerous engineering applications. Computational complexity is a key factor in determining the optimal implementation as well as real-time performance of the adaptive signal processors. To minimize the required hardware and/or software resources for implementing an adaptive filtering algorithm, it is desirable to mitigate its computational complexity as much as possible without imposing any significant sacrifice of performance. This thesis comprises a collection of thirteen peer-reviewed published works as well as an integrating material. The works are along the lines of a common unifying theme that is to devise new low-complexity adaptive filtering algorithms for communications and, more generally, signal processing applications. The main contributions are the new adaptive filtering algorithms, channel equalization techniques, and theoretical analyses listed below under four categories: 1) adaptive system identification • affine projection ...

The demand for precious radio spectrum is continuously increasing while the available radio frequency resource has become scarce due to spectrum segmentation and the dedicated frequency allocation of standardized wireless systems. This scarcity has led to the concept of cognitive radio communication which comprises a variety of techniques capable of allowing the coexistence of licensed and unlicensed systems over the same spectrum. In this context, this thesis focuses on interweave and underlay cognitive radio paradigms which are widely considered as important enablers for realising cognitive radio technology. In the interweave paradigm, an unlicensed user explores the spectral holes by means of some spectrum awareness methods and utilizes the available spectral availabilities opportunistically while in the underlay paradigm, an unlicensed user is allowed to coexist with the licensed user only if sufficient protection to the licensed user can be guaranteed. Starting ...

Estimating object motion is one of the key components of video processing and the first step in applications which require video representation. Visual object tracking is one way of extracting this component, and it is one of the major problems in the field of computer vision. Numerous discriminative and generative machine learning approaches have been employed to solve this problem. Recently, correlation filter based (CFB) approaches have been popular due to their computational efficiency and notable performances on benchmark datasets. The ultimate goal of CFB approaches is to find a filter (i.e., template) which can produce high correlation outputs around the actual object location and low correlation outputs around the locations that are far from the object. Nevertheless, CFB visual tracking methods suffer from many challenges, such as occlusion, abrupt appearance changes, fast motion and object deformation. The main reasons ...

In the last decade, one of the most significant technological developments that led to the new broadband wireless generation is the communication via multiple-input multiple-output (MIMO) systems. MIMO technologies have been adopted by many wireless standards such as Long Term Evolution (LTE), Wordlwide interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN). This is mainly due to their ability to increase the maximum transmission rates, together with the achieved reliability and coverage of current wireless communications, all without the need for additional bandwidth nor transmit power. Nevertheless, the advantages provided by MIMO systems come at the expense of a substantial increase in the cost to deploy multiple antennas and also in the receiver complexity, which has a major impact on the power consumption. Therefore, the design of low-complexity receivers is an important issue which is tackled throughout this ...

Man-made active sensing systems such as active radar and sonar have been a vital part of our civilization's advancement in navigation, defense, meteorology, and space exploration. Modern active sensing systems rely heavily on the significant progress in the science and technology of communications made within the last century. Not surprising, the fast growing communications technology has changed each and every aspect of our everyday lives. This thesis is concerned with signal design for improving the performance of active sensing and communication systems: The target detection and estimation performance of the active sensing systems can be considerably improved by a judicious design of the probing signals. Similarly, signal design has a crucial role in the implementation and efficiency of communication systems. Signal optimization for active sensing and communications usually deals with various measures of quality. This thesis focuses on several quality ...

In modern devices which provide hands-free speech capturing functionality, such as hands-free communication kits and voice-controlled devices, the received speech signal at the microphones is corrupted by background noise, interfering speech signals, and room reverberation. In many practical situations, the microphones are not necessarily located near the desired source, and hence, the ratio of the desired speech power to the power of the background noise, the interfering speech, and the reverberation at the microphones can be very low, often around or even below 0 dB. In such situations, the comfort of human-to-human communication, as well as the accuracy of automatic speech recognisers for voice-controlled applications can be signi cantly degraded. Therefore, e ffective speech enhancement algorithms are required to process the microphone signals before transmitting them to the far-end side for communication, or before feeding them into a speech recognition ...

Copy Number Alterations (CNA)s are hallmarks of cancer, which are gains or losses in copies of Deoxyribonucleic Acid (DNA) sections. Nowadays, CNAs are routinely measured by different techniques for diagnostic and prognostic purposes. The array-Comparative Genomic Hybridization (aCGH), Array-Single Nucleotide Polymorphism (aSNP) and Next Generation Sequencing (NGS) are examples of technologies that enable cost-efficient high resolution detection of CNAs. Intensive noise as well as technical and biological biases inherent to modern technologies of CNAs probing often cause inconsistency between the estimates provided by different methods. Efficient and accurate detection of the breakpoint positions in heterogeneous cancer samples measured under such conditions is a challenging practical and methodological problem. Despite the necessity of accurate CNA estimates, there is no much information regarding the estimation errors. Based on studies of the confidence limits for noisy stepwise signals, an efficient algorithm has been ...

When the transmitter of a communication system disposes of some Channel State Information (CSI), it is possible to design linear precoders that optimally allocate the power inducing high gains either in terms of capacity or in terms of reliable communications. In practical scenarios, this channel knowledge is not perfect and thus the transmitted signal suffers from the mismatch between the CSI at the transmitter and the real channel. In that context, this thesis deals with two different, but related, topics: the design of a feasible transmitter channel tracker for time varying channels, and the design of optimal linear precoders robust to imperfect channel estimates. The first part of the thesis proposes the design of a channel tracker that provides an accurate CSI at the transmitter by means of a low capacity feedback link. Historically, those schemes have been criticized because ...

Many signal and image processing applications have benefited remarkably from the theory of sparse representations. In its classical form this theory models signal as having a sparse representation under a given dictionary -- this is referred to as the "Synthesis Model". In this work we focus on greedy methods for the problem of recovering a signal from a set of deteriorated linear measurements. We consider four different sparsity frameworks that extend the aforementioned synthesis model: (i) The cosparse analysis model; (ii) the signal space paradigm; (iii) the transform domain strategy; and (iv) the sparse Poisson noise model. Our algorithms of interest in the first part of the work are the greedy-like schemes: CoSaMP, subspace pursuit (SP), iterative hard thresholding (IHT) and hard thresholding pursuit (HTP). It has been shown for the synthesis model that these can achieve a stable recovery ...