Sentence examples for linear filter estimation from inspiring English sources

Mean squared error (MSE) has been the most widely used tool to solve the linear filter estimation or system identification problem.

This paper presents a novel stochastic gradient algorithm based on the recently proposed error whitening criterion (EWC) to tackle the problem of linear filter estimation in the presence of additive white disturbances.

These consist of a channel estimator and a linear filter (for the estimation of the transmitted symbols), interconnected by a feedback loop through a symbol-wise threshold detector.

In this article, we furthermore propose a novel Equalization-based SA (ESA) structure, where the echo path is equalized by a linear filter to allow for an estimation of the loudspeaker nonlinearities by very small and efficient models.

In the fourth article, "Significance-aware filtering for nonlinear acoustic echo cancellation," by C. Hofmann, C. Huemmer, M. Guenther, and W. Kellermann, the authors propose a novel equalization-based significance-aware structure, where the echo path is equalized by a linear filter to allow for an estimation of the loudspeaker nonlinearities by very small and efficient models.

We derive a time-varying sequence of linear filters for channel estimation according to the MMSE principle.

The main blocks are an adaptive channel estimation algorithm and a linear filter.

To lower the computational complexity, Kirchner replaced the estimation of prediction weight with a linear filter using a fixed-filter coefficient.

The channel estimation is executed after the partially adaptive linear filter where the computational complexity is saved considerably.

Linear matrix inequality conditions based on a parameter-dependent Lyapunov function are proposed to design a robust linear filter which ensures an optimized upper-bound on the worst-case asymptotic estimation error variance.

In parametric PSD estimation methods, the received signal samples are modeled as the output of a linear filter to white noise.