An optimal fusion algorithm for tracking maneuvering target based on centralized structure of multisensor is proposed. This algorithm is implemented with two filters and fuzzy logic using state fusion, together with the current statistic model and adaptive filtering. Firstly, the optimal weighting coefficients are obtained using the stochastic approximation theory, a suitable method of estimati...

This paper proposes an adaptive fuzzy wavelet filter that is based on a fuzzy inference system for enhancing speech signals and improving the accuracy of speech recognition. In the last two decades, the basic wavelet thresholding algorithm has been extensively used for noise filtering. In the proposed method, adaptive wavelet thresholds are generated and controlled according to the fuzzy rules ...

0018-9251/98/$10.00 @) 1998 IEEE For a Kalman filter to operate in an optimal fashion, all filter parameters should be known exactly [ 1, 21. Incorrect selection of these parameters will result in large estimation errors or divergence [3, 41. This has motivated the design of a bank of Kalman filters with each filter using different parameters. An example is the classic Magill filter bank wherei...

in the several past years, extended kalman filter (ekf) and unscented kalman filter (ukf) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.the ukf has consistently outperformed for estimation. sometimes least estimation error doesn't yieldwith ukf for the most nonlinear systems. in this paper, we use a new approach for a two variablesta...

This paper deals with the problem of speech enhancement when a corrupted speech signal with an additive Gaussian white noise is the only information available for processing. Speech enhancement aims to improve speech quality by using various algorithms. The objective of enhancement is improvement in intelligibility and/or overall perceptual quality of degraded speech signal using audio signal p...

this paper extends the sequential learning algorithm strategy of two different types of adaptive radial basis function-based (rbf) neural networks, i.e. growing and pruning radial basis function (gap-rbf) and minimal resource allocation network (mran) to cater for on-line identification of non-linear systems. the original sequential learning algorithm is based on the repetitive utilization of s...

A necessary ingredient of an ensemble Kalman filter is covariance inflation [1], used to control filter divergence and compensate for model error. There is an ongoing search for inflation tunings that can be learned adaptively. Early in the development of Kalman filtering, Mehra [2] enabled adaptivity in the context of linear dynamics with white noise model errors by showing how to estimate the...