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 …

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 …

In a robustly adaptive Kalman filter, the key problem is to construct an adaptive factor to
balance the contributions of the kinematic model information and the measurements on the …

To use Kalman filtering for kinematic positioning and navigation, we have to deal with both
observational and kinematic models. Both of the functional models may contain global or …

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 has been applied extensively in the area of kinematic geodetic positioning.
The reliability of the linear filtering results, however, is reduced when the kinematic model …

In this paper, a robust unscented Kalman filter (UKF) based on the generalized maximum
likelihood estimation (M-estimation) is proposed to improve the robustness of the integrated …

In this contribution, an improved Extended Kalman Filter (EKF), named the Total Extended
Kalman Filter (TEKF) is proposed for integrated navigation. It can consider the neglected …

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 …

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 …