Kalman filtering is a method for estimating state variables of a dynamic systems recursively from noise-contaminated measurements. For systems with nonlinear dynamics, a natural extension of the Linear Kalman Filter (LKF), called Extended Kalman filter (EKF) is used. The Kalman filter represents one of the most popular estimation techniques for integrating signals from navigation systems, like ...

w x C y a x f x , 1 ABSTRACT In this paper, we consider chaotic communication schemes using nonlinear filtering techniques. Two different previously proposed Extended Kalman Filter based chaotic schemes are revisited and implemented using the Unscented Kalman Filter. Also, a recently proposed antipodal chaotic communication scheme is implemented with both the Extended Kalman Filter and the Unsc...

In this paper, we consider chaotic communication using nonlinear filtering techniques. The main contribution of the paper is proposing a novel antipodal chaotic communication scheme, which is implemented with both the Extended Kalman Filter and the Unscented Kalman Filter. Two different previously proposed Extended Kalman Filter based chaotic schemes are also revisited and implemented using the...

For linear dynamic systems with white process and measurement noise, the Kalman ̄lter is known to be the minimum variance linear state estimator. In the case that the random quantities are Gaussian, then the Kalman ̄lter is the minimim variance state estimator. However, in the application of Kalman ̄lters known signal information is often either ignored or dealt with heuristically. For instance...

The problem of tracking a vessel modelled at a distance by an equivalent magnetic dipole is investigated. Tracking a magnetic dipole from magnetic field measurements is a complex non-linear problem. The determination of target position, velocity and magnetic moment is formulated as an optimal stochastic estimation problem, which could be solved using the non-linear Kalman filtering methods. The...

GPS/INS integrated system is very subject to uncertainties due to exogenous disturbances, device damage, and inaccurate sensor noise statistics. Conventional Kalman filer has no robustness to address system uncertainties which may corrupt filter performance and even cause filter divergence. Based on the INS error dynamic equation, a robust Kalman filter is analyzed and applied in loosely couple...

The extended Kalman filter, which linearizes the relationship between security prices and state variables, is widely used in fixed income applications. We investigate if the unscented Kalman filter should be used to capture nonlinearities, and compare the performance of the Kalman filter to that of the particle filter. We analyze the cross section of swap rates, which are mildly nonlinear in th...

in this paper, we deal with the problem of adaptive coherent signal detection in gaussian interference (clutter plus noise) for surveillance pulse radars. some of the adaptive radar detectors exploit the ar model for clutter. most of these detectors have been obtained using the glr test. this test relies on the maximum likelihood estimation whose accuracy depends on the number of data. whereas,...

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state-variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This...