System identiication of linear dynamical systems using so-called subspace methods consists of two main steps. First, a signal subspace estimate is found. This usually corresponds to estimating the range space of the extended observability matrix. Then the system parameters are estimated from the subspace estimate. The main result of this note is explicit excitation conditions on the input signa...

This paper considers the problem of blind channel estimation of multi channel FIR lters. This is a problem arising in e.g. mobile communication systems using digital signalling. By using the orthogonality property between the noise subspace and the channel matrix, it has been shown in earlier work that the channel matrix is identiiable up to a multiplicative constant. In this article, the asymp...

a r t i c l e i n f o a b s t r a c t This paper presents a new time domain noise reduction approach based on Singular Value Decomposition (SVD) technique. In the proposed approach, the noisy signal is initially represented in a Hankel Matrix. Then SVD is applied on the Hankel Matrix to divide the data into signal subspace and noise subspace. Since singular vectors are the span bases of the mat...

We present an algorithm for computing a d-dimensional subspace of the row space of a matrix. For an n×n matrix A with m nonzero entries and with rank(A) ≥ d the algorithm generates a d × n matrix with full row rank and which is a subspace of Rows(A). If rank(A) < d the algorithm generates a rank(A)×n row-equivalent matrix. The running time of the algorithm is

Subspace fitting has become a well known method to identify FIR Single Input Multiple Output (SIMO) systems, only resorting to second-order statistics. The main drawback of this method is its computational cost, due to the eigendecomposition of the sample covariance matrix. We propose a scheme that solves the subspace fitting problem without using the eigendecomposition of the cited matrix. The...

Most existing approaches address multi-view subspace clustering problem by constructing the affinity matrix on each view separately and afterwards propose how to extend spectral clustering algorithm to handle multi-view data. This paper presents an approach to multi-view subspace clustering that learns a joint subspace representation by constructing affinity matrix shared among all views. Relyi...

1 Subspace clustering with missing data can be seen as the combination of subspace clustering and low rank matrix completion, which is essentially equivalent to high-rank matrix completion under the assumption that columns of the matrix X ∈ Rd×N belong to a union of subspaces. It’s a challenging problem, both in terms of computation and inference. In this report, we study two efficient algorith...

efficient mode shape extraction of fluid-structure systems is of particular interest in engineering. an efficient modified version of unsymmetric lanczos method is proposed in this paper. the original unsymmetric lanczos method was applied to general form of unsymmetric matrices, while the proposed method is developed particularly for the fluid-structure matrices. the method provides us with si...

Classical methods of DOA estimation such as the MUSIC algorithm are based on estimating the signal and noise subspaces from the sample covariance matrix. For a small number of samples, such methods are exposed to performance breakdown, as the sample covariance matrix can largely deviate from the true covariance matrix. We invistigate DOA estimation performance breakdown. Specifically, we consid...