Distributed Detection and Data Fusion - Varshney, Pramod K.

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Biographie:
Aditya Vempaty received the B. Tech degree in Electrical Engineering from the Indian Institute of Technology, Kanpur, India, in 2011, and the PhD degree in Electrical Engineering and Computer Science from Syracuse University, in 2015. Since August 2015, he is a Research Staff Member at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY. His research interests include human-machine inference networks, behavioral analytics, statistical signal processing, and network security. He received the All University Doctoral Prize 2016 by Syracuse University for superior achievement in completed dissertations. Bhavya Kailkhura is a Postdoctoral Researcher at the Lawrence Livermore National Labs, Livermore, CA. His research interests include high dimensional data analytics, robust statistics & control, and machine learning. He was the runner-up for Best Student Paper Award at IEEE Asilomar Conf. on Signals, Systems & Computers, 2014 and a SPS travel grant awardrecipient. He received the All University Doctoral Prize 2017 by Syracuse University for superior achievement in completed dissertations. Pramod K. Varshney is a Distinguished Professor of Electrical Engineering and Computer Science and the Director of the Center for Advanced Systems and Engineering at Syracuse University, Syracuse, NY. His current research interests include distributed sensor networks and data fusion, detection and estimation theory, wireless communications, image processing, radar signal processing, and remote sensing. He is the author of Distributed Detection and Data Fusion (Springer-Verlag, 1997). He was a James Scholar, a Bronze Tablet Senior, and a Fellow while at the University of Illinois. He is a Member of Tau Beta Pi and received the 1981 ASEE Dow Outstanding Young Faculty Award. He was elected to the grade of Fellow of the IEEE in 1997 for his contributions in the area of distributed detection and data fusion. He was the Guest Editor ofthe Special Issue on Data Fusion of the PROCEEDINGS OF THE IEEE, January 1997. In 2000, he received the Third Millennium Medal from the IEEE and Chancellor's Citation for exceptional academic achievement at Syracuse University. He received the IEEE 2012 Judith A. Resnik Award, Doctor of Engineering Honoris Causa from Drexel University in 2014, and the ECE Distinguished Alumni Award from the University of Illinois in 2015. He is on the Editorial Boards of the Journal on Advances in Information Fusion and the IEEE Signal Processing Magazine. He was the President of the International Society of Information Fusion during 2001....

Sommaire:
1 Introduction.- 1.1 Distributed Detection Systems.- 1.2 Outline of the Book.- 2 Elements of Detection Theory.- 2.1 Introduction.- 2.2 Bayesian Detection Theory.- 2.3 Minimax Detection.- 2.4 Neyman-Pearson Test.- 2.5 Sequential Detection.- 2.6 Constant False Alarm Rate (CFAR) Detection.- 2.7 Locally Optimum Detection.- 3 Distributed Bayesian Detection: Parallel Fusion Network.- 3.1 Introduction.- 3.2 Distributed Detection Without Fusion.- 3.3 Design of Fusion Rules.- 3.4 Detection with Parallel Fusion Network.- 4 Distributed Bayesian Detection: Other Network Topologies.- 4.1 Introduction.- 4.2 The Serial Network.- 4.3 Tree Networks.- 4.4 Detection Networks with Feedback.- 4.5 Generalized Formulation for Detection Networks.- 5 Distributed Detection with False Alarm Rate Constraints.- 5.1 Introduction.- 5.2 Distributed Neyman-Pearson Detection.- 5.3 Distributed CFAR Detection.- 5.4 Distributed Detection of Weak Signals.- 6 Distributed Sequential Detection.- 6.1 Introduction.- 6.2 Sequential Test Performed at the Sensors.- 6.3 Sequential Test Performed at the Fusion Center.- 7 Information Theory and Distributed Hypothesis Testing.- 7.1 Introduction.- 7.2 Distributed Detection Based on Information Theoretic Criterion.- 7.3 Multiterminal Detection with Data Compression.- Selected Bibliography.