Signal Detection and Estimation for MIMO radar and Network Time Synchronization

The theory of signal detection and estimation concerns the recovery of useful information from signals corrupted by random perturbations. This dissertation discusses the application of signal detection and estimation principles to two problems of significant practical interest: MIMO (multiple-input multiple output) radar, and time synchronization over packet switched networks. Under the first topic, we study the extension of several conventional radar analysis techniques to recently developed MIMO radars. Under the second topic, we develop new estimation techniques to improve the performance of widely used packet-based time synchronization algorithms. The ambiguity function is a popular mathematical tool for designing and optimizing the performance of radar detectors. Motivated by Neyman-Pearson testing principles, an alternative definition of the ambiguity function is proposed under the first topic. This definition directly associates with each pair of true and assumed target parameters the probability that the radar will declare a target present. We demonstrate that the new definition is better suited for the analysis of MIMO radars that perform non-coherent processing, while being equivalent to the original ambiguity function when applied to conventional radars. Based on the nature of antenna placements, transmit waveforms and the observed clutter and noise, several types of MIMO radar detectors have been individually studied in literature. A second investigation into MIMO radar presents a general method to model and analyze the detection performance of such systems. We develop closed-form expressions for a Neyman-Pearson optimum detector that is valid for a wide class of radars. Further, general closed-form expressions for the detector SNR, another tool used to quantify radar performance, are derived. Theoretical and numerical results demonstrating the value of the proposed techniques to optimize and predict the performance of arbitrary radar configurations are presented.There has been renewed recent interest in the application of packet-based time synchronization algorithms such as the IEEE 1588 Precision Time Protocol (PTP), to meet challenges posed by next-generation mobile telecommunication networks. In packet based time synchronization protocols, clock phase offsets are determined via two-way message exchanges between a master and a slave. Since the end-to-end delays in packet networks are inherently stochastic in nature, the recovery of phase offsets from message exchanges must be treated as a statistical estimation problem. While many simple intuitively motivated estimators for this problem exist in the literature, in the second part of this dissertation we use estimation theoretic principles to develop new estimators that offer significant performance benefits. To this end, we first describe new lower bounds on the error variance of phase offset estimation schemes. These bounds are obtained by re-deriving two Bayesian estimation bounds, namely the Ziv-Zakai and Weiss-Weinstien bounds, for use under a non-Bayesian formulation. Next, we describe new minimax estimators for the problem of phase offset estimation, that are optimum in terms of minimizing the maximum mean squared error over all possible values of the unknown parameters.Minimax estimators that utilize information from past timestamps to improve accuracy are also introduced. These minimax estimators provide fundamental limits on the performance of phase offset estimation schemes.Finally, a restricted class of estimators referred to as L-estimators are considered, that are linear functions of order statistics. The problem of designing optimum L-estimators is studied under several hitherto unconsidered criteria of optimality. We address the case where the queuing delay distributions are fully known, as well as the case where network model uncertainty exists.Optimum L-estimators that utilize information from past observation windows to improve performance are also described.Simulation results indicate that significant performance gains over conventional estimators can be obtained via the proposed optimum processing techniques

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc

Design optimisation of complex space systems under epistemic uncertainty

This thesis presents an innovative methodology for System Design Optimisation (SDO) through the framework of Model-Based System Engineering (MBSE) that bridges system modelling, Constrained Global Optimisation (CGO), Uncertainty Quantification (UQ), System Dynamics (SD) and other mathematical tools for the design of Complex Engineered and Engineering Systems (CEdgSs) under epistemic uncertainty. The problem under analysis has analogies with what is nowadays studied as Generative Design under Uncertainty. The method is finally applied to the design of Space Systems which are Complex Engineered Systems (CEdSs) composed of multiple interconnected sub-systems. A critical aspect in the design of Space Systems is the uncertainty involved. Much of the uncertainty is epistemic and is here modelled with Dempster Shafer Theory (DST). Designing space systems is a complex task that involves the coordination of different disciplines and problems. The thesis then proposes a set of building blocks, that is a toolbox of methodologies for the solution of problems which are of interest also if considered independently. It proposes then a holistic framework that couples these building blocks to form a SDO procedure. With regard to the building blocks, the thesis includes a network-based modelling procedure for CEdSs and a generalisation for CEdgSs where the system and the whole design process are both taken into account. Then, it presents a constraint min-max solver as an algorithmic procedures for the solution of the general Optimisation Under Uncertainty (OUU) problem. An extension of the method for the Multi-Objective Problems (MOP) is also proposed in Appendix as a minor result. A side contribution for the optimisation part refers to the extension of the global optimiser Multi Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA) with the introduction of constraint handling and multiple objective functions. The Constraint Multi-Objective Problem (CMOP) solver is however a preliminary result and it is reported in Appendix. Furthermore, the thesis proposes a decomposition methodology for the computational reduction of UQ with DST. As a partial contribution, a second approach based on a Binary Tree decomposition is also reported in Appendix. With regard to the holistic approach, instead, the thesis gives a new dentition and proposes a framework for system network robustness and for system network resilience. It finally presents the framework for the optimisation of the whole design process through the use of a multi-layer network model.This thesis presents an innovative methodology for System Design Optimisation (SDO) through the framework of Model-Based System Engineering (MBSE) that bridges system modelling, Constrained Global Optimisation (CGO), Uncertainty Quantification (UQ), System Dynamics (SD) and other mathematical tools for the design of Complex Engineered and Engineering Systems (CEdgSs) under epistemic uncertainty. The problem under analysis has analogies with what is nowadays studied as Generative Design under Uncertainty. The method is finally applied to the design of Space Systems which are Complex Engineered Systems (CEdSs) composed of multiple interconnected sub-systems. A critical aspect in the design of Space Systems is the uncertainty involved. Much of the uncertainty is epistemic and is here modelled with Dempster Shafer Theory (DST). Designing space systems is a complex task that involves the coordination of different disciplines and problems. The thesis then proposes a set of building blocks, that is a toolbox of methodologies for the solution of problems which are of interest also if considered independently. It proposes then a holistic framework that couples these building blocks to form a SDO procedure. With regard to the building blocks, the thesis includes a network-based modelling procedure for CEdSs and a generalisation for CEdgSs where the system and the whole design process are both taken into account. Then, it presents a constraint min-max solver as an algorithmic procedures for the solution of the general Optimisation Under Uncertainty (OUU) problem. An extension of the method for the Multi-Objective Problems (MOP) is also proposed in Appendix as a minor result. A side contribution for the optimisation part refers to the extension of the global optimiser Multi Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA) with the introduction of constraint handling and multiple objective functions. The Constraint Multi-Objective Problem (CMOP) solver is however a preliminary result and it is reported in Appendix. Furthermore, the thesis proposes a decomposition methodology for the computational reduction of UQ with DST. As a partial contribution, a second approach based on a Binary Tree decomposition is also reported in Appendix. With regard to the holistic approach, instead, the thesis gives a new dentition and proposes a framework for system network robustness and for system network resilience. It finally presents the framework for the optimisation of the whole design process through the use of a multi-layer network model

Aeronautical engineering: A continuing bibliography with indexes (supplement 193)

This bibliography lists 682 reports, articles and other documents introduced into the NASA scientific and technical information system in October 1985