Ensemble Kalman Filtering is a sequential Monte Carlo method commonly used in meteorology to track atmospheric states and make numerical weather predictions (NWP). With the intent to introduce statisticians to this important area of application we address some of the practical aspects of the ensemble Kalman Filter in dynamic systems. We focus on three topics related to NWP: extending the ensemb...

In this paper, we propose to combine the Kalman filter with a recent speech enhancement technique, called the phase spectrum compensation procedure, or PSC. More specifically, we apply the PSC technique to initialise the Kalman filter, whereby PSC is used to clean the noisy speech prior to LPC estimation for the Kalman recursion. We refer to the combined technique as the Kalman-PSC filter. Usin...

The Kalman Filter and its variants have been highly successful in numerous applications in technology. However, the Kalman filter is under heavy computational burden. When suffers from big data, it becomes pretty slow. On the other hand, the GPU, a processor unit with highly parallel structure, becomes more and more popular in generous purpose computing. This paper focuses on how to make Kalman...

To date, most linear and nonlinear Kalman filters (KFs) have been developed under the Gaussian assumption well-known minimum mean square error (MMSE) criterion. In order to improve robustness with respect impulsive (or heavy-tailed) non-Gaussian noises, maximum correntropy criterion (MCC) has recently used replace MMSE in developing several robust Kalman-type filters. deal more complicated nois...

In this paper we present a direct Kalman filtering approach for GPS/INS integration. In the approach, GPS and INS non–linearities are preprocessed prior to a Kalman filter. The GPS preprocessed data are taken as measurement input, while the INS preprocessed data are taken as an additional information to the Kalman filter’s state prediction. The advantage of this approach is that a simple and li...

A new Gaussian mixture filter has been developed, one that uses a re-sampling step in order to limit the covariances of its individual Gaussian components. The new filter has been designed to produce accurate solutions of difficult nonlinear/nonBayesian estimation problems. It uses static multiple-model filter calculations and Extended Kalman Filter (EKF) approximations for each Gaussian mixand...

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...