It is well known that for satellite‐based high‐precision kinematic positioning, the correct
integer ambiguities must be identified on‐the‐fly (OTF). It has also been noted that reliable …

In this paper, an optimization-based adaptive Kalman filtering method is proposed. The
method produces an estimate of the process noise covariance matrix Q by solving an …

A novel approach for vehicle tracking using a hybrid adaptive Kalman filter is proposed. The
filter utilizes recurrent neural networks to learn the vehicle's geometrical and kinematic …

The extended Kalman filter (EKF) is a widely adopted method for sensor fusion in navigation
applications. A crucial aspect of the EKF is the online determination of the process noise …

The research regarding global positioning system (GPS) vector tracking (VT), based on a
software-defined receiver (SDR), has been increasing in recent years. The strengths of VT …

The world has a complex, three-dimensional (3-D) spatial structure, but until recently the
neural representation of space was studied primarily in planar horizontal environments …

Unscented Kalman filter (UKF) is a filtering algorithm that gives sufficiently good estimation
results for the estimation problems of nonlinear systems even when high nonlinearity is in …

In recent years, unmanned aircraft systems (UASs) have played an increasingly significant
role in the military and civil fields. The flight control system, as the “hub” of an unmanned …

In this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm
optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and …

In this article, we focus on addressing the nonlinear filtering problem with unknown
measurement noise covariance and measurement outliers, which may be encountered in …