Complex factor analysis and extensions

Many subspace-based array signal processing algorithms assume that the noise is spatially white. In this case the noise covariance matrix is a multiple of the identity and the eigenvectors of the data covariance matrix are not affected by the noise. If the noise covariance is an unknown arbitrary diagonal (e.g., for an uncalibrated array), the eigenvalue decomposition leads to biased results and it has to be replaced by a more general “Factor Analysis” decomposition, which then reveals all relevant information.

In this talk we consider this data model and several extensions where the noise covariance matrix has a more general structure, such as banded, sparse, block-diagonal, and cases where we have multiple data covariance matrices that share the same noise covariance matrix.  We discuss new estimation algorithms that have much faster numerical convergence and lower complexity compared to existing algorithms for factor analysis.  Several examples to radio astronomy calibration and interference cancellation will be presented.

Bio:
Alle-Jan van der Veen is a Full Professor in Signal Processing at TU Delft, The Netherlands, and Fellow of the IEEE and of EURASIP.  He was chairman of the IEEE SPS Signal Processing for Communications Technical Committee, Editor-in-Chief of IEEE Signal Processing Letters, Editor-in-Chief of IEEE Transactions on Signal Processing, and Chairman of the IEEE SPS Fellow Reference Committee.  

At present, he is chairman of the IEEE SPS Signal Processing Methods and Techniques Technical Committee, chairman of the IEEE Kilby Medal selection committee, and Director of Publications of EURASIP.  His research interests are in the general area of system theory applied to signal processing, and in particular algebraic methods for array signal processing, with applications to wireless communications and radio astronomy.