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|Document Type: ||Thesis or dissertation|
|Title: ||Data fusion methodologies for multisensor aircraft navigation systems|
|Authors: ||Jia, Huamin|
|Supervisors: ||Allerton, David J.|
|Issue Date: ||Apr-2004|
|Abstract: ||The thesis covers data fusion for aircraft navigation systems in distributed sensor
systems. Data fusion methodologies are developed for the design, development,
analysis and simulation of multisensor aircraft navigation systems. The problems of
sensor failure detection and isolation (FDI), distributed data fusion algorithms and
inertial state integrity monitoring in inertial network systems are studied.
Various existing integrated navigation systems and Kalman filter architectures are
reviewed and a new generalised multisensor data fusion model is presented for the
design and development of multisensor navigation systems. Normalised navigation
algorithms are described for data fusion filter design of inertial network systems.
A normalised measurement model of skewed redundant inertial measurement units
(SRIMU) is presented and performance criteria are developed to evaluate optimal
configurations of SRIMUs in terms of the measurement accuracy and FDI capability.
Novel sensor error compensation filters are designed for the correction of SRIMU
measurement errors. Generalised likelihood ratio test (GLRT) methods are improved
to detect various failure modes, including short time and sequential moving-window
State-identical and state-associated fusion algorithms are developed for two forms of
distributed sensor network systems. In particular, innovative inertial network sensing
models and inertial network fusion algorithms are developed to provide estimates of
inertial vector states and similar node states. Fusion filter-based integrity monitoring
algorithms are also presented to detect network sensor failures and to examine the
consistency of node state estimates in the inertial network system.
The FDI and data fusion algorithms developed in this thesis are tested and their
performance is evaluated using a multisensor software simulation system developed
during this study programme. The moving-window GLRT algorithms for optimal
SRIMU configurations are shown to perform well and are also able to detect jump
and drift failures in an inertial network system. It is concluded that the inertial
network fusion algorithms could be used in a low-cost inertial network system and
are capable of correctly estimating the inertial vector states and the node states.|
|Appears in Collections:||PhD and Masters by research theses (School of Engineering)|
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