Kullback–Leibler divergence approach to partitioned update Kalman filter

Kullback-Leibler divergence approach to partitioned update Kalman filter

Concrete ensemble Kalman filters with rigorous catastrophic filter divergence.

The ensemble Kalman filter and ensemble square root filters are data assimilation methods used to combine high-dimensional, nonlinear dynamical models with observed data. Ensemble methods are indispensable tools in science and engineering and have enjoyed great success in geophysical sciences, because they allow for computationally cheap low-ensemble-state approximation for extremely high-dimen...

A partitioned update scheme for state-parameter estimation of distributed hydrologic models based on the ensemble Kalman filter

[1] Sequential data assimilation methods, such as the ensemble Kalman filter (EnKF), provide a general framework to account for various uncertainties in hydrologic modeling, simultaneously estimating dynamic states and model parameters with a state augmentation technique. But this technique suffers from spurious correlation for impulse responses, such as the rainfall-runoff process, especially ...

A Kalman-Filter Approach to Equalization of CDMA Downlink Channels

An efficient method for equalization of downlink CDMA channels is presented. By describing the observed signal in terms of a state-space model, the method employs the Kalman filter (KF) to achieve an unbiased signal estimate satisfying the linear minimum mean-squared error (LMMSE) criterion. The state-space model is realized at the symbol and chip levels. With the symbollevel model, the KF is u...

A Kalman Filter-Based Update Scheme for Road Following

A novel technique for tracking arbitrary colored objects in complex environments is proposed in this paper. Active search and prediction of search area is used for improving speed. The system is capable of accurately tracking a t the rate of around 10 frames per second without any special hardware.