In many practical Kalman filter applications, the quantity of most significance for the estimation e m r w the process noise matrix. When filters are stabilized or performance is sought improved, tuning of this matrix is the most common method. This tuning process cannot be done before the filter is implemented, OS it is primarily made necessary by modelling errors. In this paper two different ...

It is known that the Kalman filter estimation performance heavily depends on the statistical parameters to both the dynamic and observation models, especially the covariance matrix Q. This paper presents a fast genetic algorithm (GA) to adapt extended Kalman filter (EKF) for real time tracking. In the proposed method, the covariance matrix Q can be real-time adjusted by GA to meet some specific...

A new square root state estimation algorithm is introduced, that operates in the information mode in both the time and the measurement update stages. The algorithm, called the V-Lambda filter, is based on the spectral decomposition of the covariance matrix into a V\V form, where V is the matrix whose columns are the eigenvectors of the covariance matrix, and A is the diagonal matrix of its eige...

This paper studies the distributed state estimation problem for a class of discrete time-varying systems over sensor networks. Firstly, it is shown that a networked Kalman filter with optimal gain parameter is actually a centralized filter, since it requires each sensor to have global information which is usually forbidden in large networks. Then, a sub-optimal distributed Kalman filter (DKF) i...

The state-covariance of a linear filter is characterized by a certain algebraic commutativity property with the state matrix of the filter, and also imposes a generalized interpolation constraint on the power spectrum of the input process. This algebraic property and the relationship between state-covariance and the power spectrum of the input allow the use of matrix pencils and analytic interp...

This paper presents a procedure for deriving linear filters which are based on matrix-vector multiplication instead of linear convolution and which can be designed to match the frequency responses of linear equiripple FIR filters. The magnitude of the matrix filter response is matched to the magnitude response of a given linear FIR filter by solving a set of nonlinear equations numerically usin...

This letter shows some counter-intuitive simulation results that the symbolic sequences and the state variables of a digital filter with two’s complement arithmetic and arbitrary initial conditions and order will be eventually zero when all the filter parameters are even numbers, no matter the system matrix of the filter is stable or not.

In this paper, we deal with the problem of designing a filter for discrete-time systems subject to error variance and circular pole constraints. Specifically, we aim to design a filter such that the norm of the filtering error-transfer function is not less than a given upper bound, while the poles of the filtering matrix are assigned within a prespecified circular region, and the steady-state e...

A prerequisite to understand neuronal function and characteristic is to classify neuron correctly. The existing classification techniques are usually based on structural characteristic and employ principal component analysis to reduce feature dimension. In this work, we dedicate to classify neurons based on neuronal morphology. A new feature selection method named binary matrix shuffling filter...

The possible methodologies to handle the uncertain parameter are reviewed. The core idea of the desensitized Kalman filter is introduced. A new cost function consisting of a posterior covariance trace and trace of a weighted norm of the state error sensitivities matrix is minimizing to obtain a well-known analytical gain matrix, which is different from the gain of the desensitized Kalman filter...