High dynamic aircraft is a very attractive new generation vehicles, in which provides near space aviation with large flight
envelope both speed and altitude, for example the hypersonic vehicles. The complex flight environments for high
dynamic vehicles require high accuracy and stability navigation scheme. Since the conventional Strapdown Inertial
Navigation System (SINS) and Global Position System (GPS) federal integrated scheme based on EKF (Extended
Kalman Filter) is invalidation in GPS single blackout situation because of high speed flight, a new high precision and
stability integrated navigation approach is presented in this paper, in which the SINS, GPS and Celestial Navigation
System (CNS) is combined as a federal information fusion configuration based on nonlinear Unscented Kalman Filter
(UKF) algorithm. Firstly, the new integrated system state error is modeled. According to this error model, the SINS
system is used as the navigation solution mathematic platform. The SINS combine with GPS constitute one error
estimation filter subsystem based on UKF to obtain local optimal estimation, and the SINS combine with CNS constitute
another error estimation subsystem. A non-reset federated configuration filter based on partial information is proposed to
fuse two local optimal estimations to get global optimal error estimation, and the global optimal estimation is used to
correct the SINS navigation solution. The χ 2 fault detection method is used to detect the subsystem fault, and the fault
subsystem is isolation through fault interval to protect system away from the divergence. The integrated system takes
advantages of SINS, GPS and CNS to an immense improvement for high accuracy and reliably high dynamic navigation
application. Simulation result shows that federated fusion of using GPS and CNS to revise SINS solution is reasonable
and availably with good estimation performance, which are satisfied with the demands of high dynamic flight navigation.
The UKF is superior than EKF based integrated scheme, in which has smaller estimation error and quickly convergence
rate.
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