In this paper, we present a new algorithm for tracking the signal subspace recursively. It is based on a new interpretation of the signal subspace. We introduce a novel information criterion for signal subspace estimation. We show that the solution of the proposed constrained optimization problem results the signal subspace. In addition, we introduce three adaptive algorithms which can be used ...

Detecting the presence of subspace signals with unknown clutter (or interference) is a widely known difficult problem encountered in various signal processing applications. Traditional methods fails to solve this problem because they require knowledge of clutter subspace, which has to be learned or estimated beforehand. In this paper, we propose a novel detector, named volume-correlation subspa...

A generalized subspace approach is proposed for enhancement of speech corrupted by colored noise. A nonunitary transform, based on the simultaneous diagonalization of the clean speech and noise covariance matrices, is used to project the noisy signal onto a signal-plus-noise subspace and a noise subspace. The clean signal is estimated by nulling the signal components in the noise subspace and r...

We derive multi-rank generalizations of the MVDR beamformer to separate an unknown signal of interest in the presence of interference and noise. The spatial signature of the signal is assumed to lie in a known linear subspace, but the orientation of the signal in that subspace is otherwise unknown. The unknown orientation may be fixed for a sequence of experimental realizations, in which case t...

In this paper, a new tensor-based subspace approach is proposed to estimate the direction of departure (DOD) and the direction of arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise. Firstly, the received signals can be packed into a third-order measurement tensor by exploiting the inherent structure of the matched filter. Then, the me...

Linear discriminant analysis (LDA) is a well-known scheme for supervised subspace learning. It has been widely used in the applications of computer vision and pattern recognition. However, an intrinsic limitation of LDA is the sensitivity to the presence of outliers, due to using the Frobenius norm to measure the inter-class and intra-class distances. In this paper, we propose a novel rotationa...