New approaches to the development of data fusion algorithms for inertial network
systems are described. The aim of this development is to increase the accuracy
of estimates of inertial state vectors in all the network nodes, including the
navigation states, and also to improve the fault tolerance of inertial network
systems. An analysis of distributed inertial sensing models is presented and new
distributed data fusion algorithms are developed for inertial network systems.
The distributed data fusion algorithm comprises two steps: inertial measurement
fusion and state fusion. The inertial measurement fusion allows each node to
assimilate all the inertial measurements from an inertial network system, which
can improve the performance of inertial sensor failure detection and isolation
algorithms by providing more information. The state fusion further increases the
accuracy and enhances the integrity of the local inertial states and navigation
state estimates. The simulation results show that the two-step fusion procedure
overcomes the disadvantages of traditional inertial sensor alignment procedures.
The slave inertial nodes can be accurately aligned to the master node
