A novel sparse Gauss–Hermite quadrature filter is proposed using a sparse-grid method for multidimensional numerical integration in the Bayesian estimation framework. The conventional Gauss–Hermite quadrature filter is computationally expensive for multidimensional problems, because the number of Gauss–Hermite quadrature points increases exponentially with the dimension. The number of sparse-gr...

Recovering method of missing data based on the proposed modified Kalman filter for the case that the time series of mean data is know is proposed. There are some cases of which although a portion of data is missing, mean value of the time series of data is known. For instance, although coarse resolution of imagery data are acquired every day, fine resolution of imagery data are missing sometime...

A novel robust finite-horizon Kalman filter is presented for networked linear time-varying systems with norm-bounded parameter uncertainty whether, or not, the data packets in the network are time-stamped. Measured data loss and latency in the communication link are both described by a Bernoulli distributed random sequence. Then, a two-stage recursive structure is employed for the robust Kalman...

In this paper we present a neural network extended Kalman filter for modeling noisy financial time series. The neural network is employed to estimate the nonlinear dynamics of the extended Kalman filter. Conditions for the neural network weight matrix are provided to guarantee the stability of the filter. The extended Kalman filter presented is designed to filter three types of noise commonly o...

In this correspondence, the authors develop a novel method based on spherical radial cubature and Gauss–Laguerre quadrature rule for non-linear state estimation problems. The proposed filter, referred as cubature quadrature Kalman filter (CQKF) would be able to overcome inherent disadvantages associated with the earlier reported cubature Kalman filter (CKF). The theory and formulation of CQKF h...

Using direction vectors of unit length as measurements for attitude estimation in an extended Kalman filter inevitably results in a singular measurement covariance matrix. Singularity of the measurement covariance means no noise is present in one component of the measurement. Unit vector measurements have no noise in the radial component. Singular measurement covariances can be dealt with by th...

Formal code certification is a rigorous approach to demonstrate software quality. Its basicidea is to require that code producers provide formal certificates, or proofs, that their codesatisfies certain quality properties. In this paper, we focus on certifying software developed forstate estimation of dynamic systems, which is an important problem found in spacecraft, aircraft,g...

In the field of motion estimation for surveillance video, various techniques have been applied. One of the common approaches is Kalman filtering technique and it is interesting to explore the extension of this technique for the prediction and estimation of motion via the image sequences. In this paper, a moving object tracking in surveillance video using Kalman filter is proposed. The typical K...

Kalman filter based hybrid estimation algorithms have been used in many applications. However, performance analysis of these algorithms is difficult because many of these algorithms use a set of Kalman filters that are coupled with each other. We present an algorithm to compute the means and cross-covariances of the residuals of the set of coupling (or interacting) Kalman filters. Specifically,...