We consider non-linear state space models in high-dimensional situations, where the two common tools for state space models both have difficulties. The Kalman filter variants are seriously biased due to non-Gaussianity and the particle filter suffers from the “curse of dimensionality”. Inspired by a regression perspective on the Kalman filter, a novel approach is developed by combining the Kalm...

This is a survey paper.The performance of the Kalman filter (KF), which is Algoritstandard as an outstanding implementation for dynamic system state estimation, greatly depends on its parameter R, called the measurement noise covariance matrix. . However, it’s difficult to obtain the accurate value of R before the filter starts, and the value of R is possible to change with the measurement envi...

The main aim of the Speech Enhancement algorithms is to improve the Quality of speech. The Quality of speech is expressed in two parameters. One is clarity, and another is intelligibility. In this paper, we proposed a method to improve the quality of speech based on computationally efficient AR modeled Iterative Kalman Filter with time and frequency mask. This approach is based on reconstructio...

The goal of this study is to compare the performances of the ensemble Kalman filter and a reduced-rank extended Kalman filter when applied to different dynamic regimes. Data assimilation experiments are performed using an eddy-resolving quasi-geostrophic model of the winddriven ocean circulation. By changing eddy viscosity, this model exhibits two qualitatively distinct behaviors: strongly chao...

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...