Data fusion methodologies for multisensor aircraft navigation systems
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 GLRT algorithms. 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.