Interior sampling and exterior sampling (or enclosure) signal-subspace-based imaging methodologies for extended scatterers derived in previous work are reformulated and reinterpreted in terms of the concepts of angles and distances between subspaces. The insight gained from this reformulation renders a broader, more encompassing inversion methodology based on a (pseudo) cross-coherence matrix a...

In continuous wave (CW) radar and high pulse repetition frequency pulse-Doppler (HPRF-PD) radar, the interference plus noise sample snapshots are hard to be obtained. The desired signal in the received snapshots makes the LCMV-based adaptive monopulse algorithm sensitive to pattern look direction error. A linearly constrained subarray robust adaptive monopulse algorithm based on main lobe maint...

BACKGROUND The Eigenspace-based beamformers, by orthogonal projection of signal subspace, can remove a large part of the noise, and provide better imaging contrast upon the minimum variance beamformer. However, wrong estimate of signal and noise component may bring dark-spot artifacts and distort the signal intensity. The signal component and noise and interference components are considered unc...

We present a method for learning a low-dimensional manifold for speech from clean speech samples in high-dimensional space. Using this manifold, we perform speech denoising by projecting noisy speech onto the manifold to remove nonspeech components. This method of denoising classifies our algorithm as a signal subspace denoising method, where highdimensional noisy data is projected onto the sig...

Robust adaptive beamforming (RAB) is essential in many applications to ensure signal-receiving quality when model errors exist. Eigen-subspace projection (ESP), one of the most popular RAB methods, can be used there are arbitrary errors. However, a major challenge ESP subspace selection. Traditional (TESP) treats signal as subspace; thus, source enumeration required obtain prior information. An...

In this paper, we present a new algorithm for tracking the generalized signal subspace recursively. It is based on an interpretation of the generalized signal subspace as the solution of a constrained minimization task. This algorithm, referred to as the CGST algorithm, guarantees the Cx-orthonormality of the estimated generalized signal subspace basis at each iteration which Cx denotes the cor...

In this paper a class of subspace tracking algorithms applicable to, for example, sensor array signal processing is presented. The basic idea of the algorithm is to reduce the amount of computations required for an exact SVD/EVD update, using a certain perturbation strategy. Perhaps the most remarkable property of the proposed algorithms is that the same class of algorithm can be applied to the...

Our aim in this paper is to extend the matched subspace detectors (MSDs) of [1–3] to the detection of stochastic signals. In [1–3] the signal to be detected was assumed to be placed deterministically at an unknown location in a known signal subspace. The basis for the subspace was irrelevant. In this paper the signal is assumed to be placed randomly at an unknown location in a known subspace. I...