| dc.description.abstract | Research on multisensor data fusion aims at providing the enabling technology to combine
information from several sources in order to form a unifi ed picture. The literature
work on fusion of conventional data provided by non-human (hard) sensors is vast and
well-established. In comparison to conventional fusion systems where input data are generated
by calibrated electronic sensor systems with well-defi ned characteristics, research
on soft data fusion considers combining human-based data expressed preferably in unconstrained
natural language form. Fusion of soft and hard data is even more challenging, yet
necessary in some applications, and has received little attention in the past. Due to being
a rather new area of research, soft/hard data fusion is still in a
edging stage with even
its challenging problems yet to be adequately de fined and explored.
This dissertation develops a framework to enable fusion of both soft and hard data
with the Random Set (RS) theory as the underlying mathematical foundation. Random
set theory is an emerging theory within the data fusion community that, due to its powerful
representational and computational capabilities, is gaining more and more attention among
the data fusion researchers. Motivated by the unique characteristics of the random set
theory and the main challenge of soft/hard data fusion systems, i.e. the need for a unifying
framework capable of processing both unconventional soft data and conventional hard data,
this dissertation argues in favor of a random set theoretic approach as the first step towards
realizing a soft/hard data fusion framework.
Several challenging problems related to soft/hard fusion systems are addressed in the
proposed framework. First, an extension of the well-known Kalman lter within random
set theory, called Kalman evidential filter (KEF), is adopted as a common data processing
framework for both soft and hard data. Second, a novel ontology (syntax+semantics)
is developed to allow for modeling soft (human-generated) data assuming target tracking
as the application. Third, as soft/hard data fusion is mostly aimed at large networks of
information processing, a new approach is proposed to enable distributed estimation of
soft, as well as hard data, addressing the scalability requirement of such fusion systems.
Fourth, a method for modeling trust in the human agents is developed, which enables the
fusion system to protect itself from erroneous/misleading soft data through discounting
such data on-the-fly. Fifth, leveraging the recent developments in the RS theoretic data
fusion literature a novel soft data association algorithm is developed and deployed to extend
the proposed target tracking framework into multi-target tracking case. Finally, the
multi-target tracking framework is complemented by introducing a distributed classi fication
approach applicable to target classes described with soft human-generated data.
In addition, this dissertation presents a novel data-centric taxonomy of data fusion
methodologies. In particular, several categories of fusion algorithms have been identifi ed
and discussed based on the data-related challenging aspect(s) addressed. It is intended to
provide the reader with a generic and comprehensive view of the contemporary data fusion
literature, which could also serve as a reference for data fusion practitioners by providing
them with conducive design guidelines, in terms of algorithm choice, regarding the specifi c
data-related challenges expected in a given application. | en |
