Showing papers by "Mats Viberg published in 2005"

Subspace based approaches for Wiener system identification

Abstract: We consider the problem of Wiener system identification in this note. A Wiener system consists of a linear time invariant block followed by a memoryless nonlinearity. By modeling the inverse of the memoryless nonlinearity as a linear combination of known nonlinear basis functions, we develop two subspace based approaches, namely an alternating projection algorithm and a minimum norm method, to solve for the Wiener system parameters. Based on computer simulations, the algorithms are shown to be robust in the presence of modeling error and noise.

Data-adaptive array interpolation for DOA estimation in correlated signal environments

Abstract: Many popular direction-of-arrival (DOA) estimators rely on the fact that the array response vector of the array is Vandermonde, for example, that of a uniform linear array (ULA). Array interpolation is a preprocessing technique to transform the array response vector of a planar array of arbitrary geometry to that of a ULA over an angular sector. While good approximation within the target sector is attained with the various existing array interpolation approaches, the response of the interpolated array in the out-of-sector region is at best partially controlled. Accordingly, out-of-sector signals, especially those highly correlated with the in-sector signals, can degrade significantly the performance of DOA estimators that rely on the Vandermonde form to work correctly. Recently, we proposed an improved array interpolation approach that takes into account the array response over the full azimuth. In this paper, we develop the idea further and present a simple data-adaptive array interpolation scheme that can provide significantly better accuracy in the DOA estimates. We present examples to demonstrate the effectiveness of our proposal.

Two new approaches to channel prediction based on sinusoidal modelling

Abstract: Prediction of communication channels is important, for example to enable adaptive transmission strategies. In this paper, two new predictors are proposed based on sinusoidal modelling. First a combined LMMSE (given frequency estimates) and LP is proposed. Then an improved LMMSE prediction is proposed by modelling the frequency estimate errors as Gaussian random variables. Both predictions have better performances than the LMMSE prediction (given frequency estimates) in computer simulations