An adaptive integrated Kalman filtering based on the adjustment outputs of local navigation
sensors and the outputs of a dynamic or kinematic state model is presented, which avoids …

Some constraints exist in the kinematic state parameters used for integrated navigation; if
these are taken into account, the accuracy of positioning and navigation can be improved …

When certain constraints in the kinematic state parameters of a multi-sensor navigation
system exist, they should be taken into account for the improvement of the positioning …

The quality of kinematic navigation and positioning depends on the quality of the model
describing the vehicle movements and the reliability of the observations. An adaptive …

An efficient signal fusion method is put forward for the integrated navigation of the multiple
sensor system. To show the correlations of the master filter and the local filters, the …

As a form of optimal estimator characterized by recursive evaluation, the Kalman filter
(KF)(Bar-Shalom, et al, 2001; Brown and Hwang, 1997, Gelb, 1974; Grewal & Andrews …

Autonomous navigation using a Kalman filter requires, in addition to an a priori knowledge
of measurement data accuracy, an initial estimate of six orbit parameters and a six-by-six …

A new adaptively robust Kalman filtering was developed in 2001. The main achievements of
the adaptively robust filter are summarized from the published papers in recent years. These …

The Kalman filter (KF) is a form of optimal estimator characterized by recursive evaluation,
which has been widely applied to the navigation sensor fusion. Utilizing the KF requires that …

An integrated navigation information system must know continuously the current position
with a good precision. The required performance of the positioning module is achieved by …