The key problems in applying the adaptively robust filtering to navigation are to establish an
equivalent weight matrix for the measurements and a suitable adaptive factor for balancing …

An adaptively robust filter with multi adaptive factors is proposed, based on the principles of
adaptive Kalman filter and bifactor robust estimation for correlated observations. The …

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 …

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 …

TO SAVE the manual efforts and costs spent on the ground station system, and to enhance
the survivability of vehicles, autonomous systems including guidance, navigation, and …

A key problem for an adaptive filter is to establish a suitable adaptive factor for balancing the
contributions of the measurements and the predicted state information from some kinematic …

An adaptive filtering based on moving window covariance estimation is introduced after the
shortcomings of covariance matrices formed by windowing residual vectors, innovation …

In order to control the influences of outlying measurements and the kinematic model errors
on the integrated navigation results, a robust estimation method and an adaptive data fusion …

The precise positioning of dynamic and static objects such as vehicles and pedestrians is a
key technology. A global navigation satellite system signal is the primary signal source …