Distributed Detection and Fusion in Parallel Sensor Architectures

Parallel distributed detection system consists of several separate sensor-detector nodes (separated spatially or by their principles of operation), each with some processing capabilities. These local sensor-detectors send some information on an observed phenomenon to a centrally located Data Fusion Center for aggregation and decision making. Often, the local sensors use electro-mechanical, optical or RF modalities and are known as ``hard'' sensors. For such data sources, the sensor observations have structure and often some tractable statistical distributions which help in weighing their contribution to an integrated global decision. In a distributed detection environment, we often also have ``humans in the loop.''. Humans provide their subjective opinions on these phenomena. These opinions are labeled ``soft'' data. It is of interest to integrate "soft'' decisions, mostly assessments provided by humans, with data from the "hard" sensors, in order to improve global decision reliability. Several techniques were developed to combine data from traditional hard sensors, and a body of work was also created about integration of "soft'' data. However relatively little work was done on combining hard and soft data and decisions in an integrated environment. Our work investigates both "hard'' and "hard/soft'' fusion schemes, and proposes data integration architectures to facilitate heterogeneous sensor data fusion. In the context of "hard'' fusion, one of the contributions of this thesis is an algorithm that provides a globally optimum solution for local detector (hard sensor) design that satisfies a Neyman-Pearson criterion (maximal probability of detection under a fixed upper bound on the global false alarm rate) at the fusion center. Furthermore, the thesis also delves into application of distributed detection techniques in both parallel and sequential frameworks. Specifically, we apply parallel detection and fusion schemes to the problem of real time computer user authentication and sequential Kalman filtering for real time hypoxia detection. In the context of "hard/soft'' fusion, we propose a new Dempster-Shafer evidence theory based approach to facilitate heterogeneous sensor data fusion. Application of the framework to a number of simulated example scenarios showcases the wide range of applicability of the developed approach. We also propose and develop a hierarchical evidence tree based architecture for representing nested human opinions. The proposed framework is versatile enough to deal with both hard and soft source data using the evidence theory framework, it can handle uncertainty as well as data aggregation.Ph.D., Electrical Engineering -- Drexel University, 201

Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well