Solving the SVD updating problem for subspace tracking on a xed sized linear array of processors

Many signal processing algorithms are based on the computation of the eigenstructure (eigenvalues and eigenvectors) of the covariance matrix of a data matrix. Applications include: direction of arrival estimation in array processing, spectral estimation and CDMA synchronization [1]. The advantages of using the eigenvector-based methods (also called subspace based methods) are well-known. In the usual way of using such algorithms, the covariance matrix is rst estimated from the received data. Then, by using some numerical method, the Eigen Value Decomposition (EVD) is computed before applying a subspace based algorithm, such as, MUSIC [6]. Before applying the subspace based algorithm, the EVD is used to separate the signal subspace from the noise subspace. The covariance matrix is estimated as : R̂n = Y 0 nYn; (1)