a r t i c l e i n f o a b s t r a c t Keywords: Adaptation Unscented Kalman filter Masreliez–Martin filter Relative navigation Robot arm tracking Unscented Kalman filter (UKF) has been extensively used for state estimation of nonlinear stochastic systems, which suffers from performance degradation and even divergence when the noise distribution used in the UKF and the truth in a real system are...

Kalman filter is a well known adaptive filtering Algorithm, widely used for target tracking applications. When the system model and measurements are non linear, variation of Kalman filter like extended Kalman filter (EKF) and Unscented Kalman filters (UKF) are used. For obtaining reliable estimate of the target state, filter has to be tuned before the operation (off line).Tuning an UKF is the p...

The Extended Kalman Filter (EKF) is a well established technique for position and velocity estimation. However, the performance of the EKF degrades considerably in highly non-linear system applications as it requires local linearisation in its prediction stage. The Unscented Kalman Filter (UKF) was developed to address the non-linearity in the system by deterministic sampling. The UKF provides ...

Combined with strong tracking filter (STF) theory, the Strong Tracking Square-Root Unscented Kalman Filter (UKF)-based satellite attitude determination algorithm is proposed in this paper. QR decomposition and Cholesyk decomposition are introduced in this paper, which improves the stability of filter. In addition, by introduced adaptive fading factor, the prediction error covariance matrix can ...

Based on presentation of the principles of the EKF and UKF for state estimation, we discuss the differences of the two approaches. Four rather different simulation cases are considered to compare the performance. A simple procedure to include state constraints in the UKF is proposed and tested. The overall impression is that the performance of the UKF is better than the EKF in terms of robustne...

We consider the problem of nonlinear filtering under the circumstance of unknown covariance statistic of the measurement noise. A novel adaptive unscented Kalman filter (UKF) integrating variational Bayesian methods and fuzzy logic techniques is proposed in this paper. It is called fuzzy adaptive variational Bayesian UKF (FAVBUKF). Firstly, the sufficient statistics of the measurement noise var...

In order to improve filtering precision and restrain divergence caused by sensor faults or model mismatches for target tracking, a new adaptive unscented Kalman filter (N-AUKF) algorithm is proposed. First of all, the unscented Kalman filter (UKF) problem to be solved for systems involving model mismatches is described, after that, the necessary and sufficient condition with third order accurac...

Abstract. This paper describes a method for estimating sensor biases by using a lowdimensional Unscented Kalman Filter (UKF) to maintain the positional estimation accuracy of an autonomous vehicle (AV). It is difficult to estimate attitude accurately in a blind situation (such as with no GPS satellites and no landmarks), because of sensor bias. We developed a dead reckoning system for an embedd...

In this note, we illustrate the effect of nonlinear state propagation in the unscented Kalman filter (UKF). We consider a simple nonlinear system, consisting of a two-axis inertial measurement unit. Our intent is to show that the propagation of a set of sigma points through a nonlinear process model in the UKF can produce a counterintuitive (but correct) updated state estimate. We compare the r...

In this paper, the approximation of nonlinear systems using unscented Kalman filter (UKF) is discussed, and the conditions for the convergence of the UKF are derived. The detection of faults from residuals generated by the UKF is presented. As fault detection often reduced to detecting irregularities in the residuals, such as the mean, the local approach, a powerful statistical technique to det...