An unscented Kalman filter (UKF) for matrix Lie groups is proposed where the time propagation of state formulated on algebra. This done with kinematic differential equation logarithm, inverse right Jacobian used. The sigma points can then be expressed as logarithms in vector form, and computation mean covariance resulting formulation to a large extent based form is, therefore, closer UKF system...

A convergence analysis of the modified unscented Kalman filter (UKF), used as an observer for a class nonlinear deterministic continuous time systems, is presented. Under certain conditions, extended (EKF) exponential non-linear i.e., dynamics estimation error exponentially stable. It shown that unlike EKF, UKF not converging observer. modification – proposed, which better candidate This paper ...

In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking. The proposed method, called the Gaussian mixture measurements-probability hypothesis density (GMM-PHD) filter, not only approximates the posterior intensity using a Gaussian mixture, but also models the likelihood functi...

Nonlinear state estimation and fusion tracking are always hot research topics for information processing. Compared to linear fusion tracking, nonlinear fusion tracking takes many new problems and challenges. Especially, the performances of fusion tracking, based on different nonlinear filters, are obviously different. The conventional nonlinear filters include extended Kalman filter (EKF), unsc...

The unscented Kalman filter (UKF) is adopted in the interacting multiple model (IMM) framework to deal with the system nonlinearity in navigation applications. The adaptive tuning system (ATS) is employed for assisting the unscented Kalman filter in the IMM framework, resulting in an interacting multiple model adaptive unscented Kalman filter (IMM-AUKF). Two models, a standard UKF and an adapti...

Bearings-only tracking (BOT) plays a vital role in underwater surveillance. In BOT, measurement is tangentially related to state of the system. This also corrupted with noise due turbulent environment. Hence estimation process using BOT becomes nonlinear. necessitates use nonlinear filtering algorithms place traditional linear filters like Kalman filter. general, these utilize assumption measur...

The univariate non-stationary growth model (UNGM) is widely used in the verification of nonlinear filters, and unscented Kalman filter (UKF) often as reference for comparative analysis when using this to evaluate performance. However, due strong nonlinearity UNGM change properties with different parameter settings, estimation misalignment problem reasons will occur UKF filtering. To solve these...

The cubature Kalman filter (CKF), which is based on the third degree spherical–radial cubature rule, is numericallymore stable than the unscented Kalman filter (UKF) but less accurate than theGauss–Hermite quadrature filter (GHQF). To improve the performance of the CKF, a new class of CKFs with arbitrary degrees of accuracy in computing the spherical and radial integrals is proposed. The third-...

The present document aims at providing a short, didactical introduction to three standard versions of the Kalman filter, namely its variants identified as Basic, Extended , and Unscented. The application of these algorithms in three representative problems is discussed.

Satellites provide various services essential to the modern life of human being. For example, satellite images are used for many applications such as reconnaissance, geographic information system, etc. Therefore, design and operation requirements of the satellite system have become more severe, and also the system reliability during the operation is required. Satellite attitude control systems ...