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

Information

Author
Leonard, Mark Ryan
Institution
Technische Universit├Ąt Darmstadt
Supervisors
Publication Year
2019
Upload Date
March 4, 2019

First few pages / click to enlarge

The current layout is optimized for mobile phones. Page previews, thumbnails, and full abstracts will remain hidden until the browser window grows in width.

The current layout is optimized for tablet devices. Page previews and some thumbnails will remain hidden until the browser window grows in width.