The Kalman filter cannot be used with nonstationary state space models. To circumvent this difficulty, a conditional state space model and a new algorithm, calIed the conditional Kalman filter, can be used. The conditional state space model is obtained by first selecting adequately that part oof the initial state vector which has an unspecified distribution and then conditioning on O. Using the...

The paper put forward using BP neural network and Kalman filter to signal processing of MEMS inertial sensors. This paper uses Kalman filter value for information fusion of gyroscope and accelerometer, and the attitude angle is accurate. The state and observation equation of attitude angle measuring system with characteristic of BP neural network, and the design of the Kalman filter is simple a...

A model based fault detection and diagnosis technique for DC motor is proposed in this paper. Fault detection using Kalman filter and its different variants are compared. Only incipient faults are considered for the study. The Kalman Filter iterations and all the related computations required for fault detection and fault confirmation are presented. A second order linear state space model of DC...

This paper presents a core approach to design and develop a real-time based Vehicle Tracking System using Kalman filter. It is used to determine the current location of a target device in terms of UTC time , Data status, latitude, longitude, UTC date, Speed over ground in knots, Magnetic variation ,Mode indicators and Checksum information by utilizing the sms features of GSM technology. An atte...

Kalman filters provide an important technique for estimating the states of engineering systems. With several variations of nonlinear Kalman filters, there is a lack of guidelines for filter selection with respect to a specific research or engineering application. This creates a need for an in-depth discussion of the intricacies of different nonlinear Kalman filters. Particularly of interest for...

An accurate Linear Regression Kalman Filter (LRKF) for nonlinear systems called Smart Sampling Kalman Filter (S2KF) is introduced. In order to get a better understanding of this new filter, a general introduction to Nonlinear Kalman Filters based on statistical linearization and LRKFs is given. The S2KF is based on a new low-discrepancy Dirac mixture approximation of Gaussian densities. This ap...

We compare the performance of three filters applied to a problem in computer vision, the estimation of the position and velocity of a uniformly translating point, using image measurements corrupted by Gaussian noise. We choose this very simple application in order to make the comparison between the three filters as clear as possible. The filters are (i) the optimal filter [1]; (ii) a second ord...

In this book, the extended Kalman filter (EKF) has been used as the standard technique for performing recursive nonlinear estimation. The EKF algorithm, however, provides only an approximation to optimal nonlinear estimation. In this chapter, we point out the underlying assumptions and flaws in the EKF, and present an alternative filter with performance superior to that of the EKF. This algorit...

Target tracking is very important field of research as it has wider applications in defense as well as civilian applications. Kalman filter is generally used for such applications. When the process and measurements are non linear extensions of Kalman filters like Extended Kalman Filter, Unscented Kalman Filters are widely used. UKF can give estimations up to second order characteristics of rand...

Visual servoing has been around for decades, but time delay is still one of the most troublesome problems to achieve target tracking. To circumvent the problem, in this paper, the Kalman filter is employed to estimate the future position of the object. In order to introduce the Kalman filter, accurate time delays, which include the processing lag and the ...