Abstract / truncated to 115 words (read the full abstract)

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 ... toggle 9 keywords

signal processing robustness distributed sensor networks sequential detection sequential hypothesis testing dempster-shafer theory multi-target tracking phd filter particle filter


Leonard, Mark Ryan
Technische Universit├Ąt Darmstadt
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March 4, 2019

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