We describe ways to define and calculate L1-norm signal subspaces which are less sensitive to outlying data than L2-calculated subspaces. We focus on the computation of the L1 maximum-projection principal component of a data matrix containing N signal samples of dimension D and conclude that the general problem is formally NP-hard in asymptotically large N , D. We prove, however, that the case ...

A signal-subspace method is derived for the localization and imaging of unknown scatterers using intensity-only wave field data (lacking field phase information). The method is an extension of the time-reversal multiple-signal-classification imaging approach to intensity-only data. Of importance, the derived methodology works within exact scattering theory including multiple scattering.

Signal detection in certain noise environments fits naturally into a statistical hypothesis testing framework. In order to have moderately tractable models, the noise is often assumed to be additive with a multivariate normal distribution. Additionally, computational complexity requirements may demand the assumption of spatial stationarity, particularly in the case when the data is 2-dimensiona...

Abstract We describe ways to define and calculate L1-norm signal subspaces which are less sensitive to outlying data than L2-calculated subspaces. We start with the computation of the L1 maximum-projection principal component of a data matrix containing N signal samples of dimension D. We show that while the general problem is formally NP-hard in asymptotically large N , D, the case of engineer...

A mathematical framework for data representation and for noise reduction is presented in this paper. The basis of the approach lies in the use of wavelets derived from the general theory of frames to construct a subspace capable of representing the original signal excluding the noise. The representation subspace is shown to be efficient in signal modeling and noise reduction, but it may be acco...

The Signal-to-Interference ratio (SIR) has been highlighted in the literature to be a most eecient criterion for several methods aiming at reducing the eeects of cochannel interference, e.g. diversity reception, dynamic channel allocation and power control. In this paper we address the problem on how to obtain fast and accurate measurements of this parameter in a practical context. We develop a...

In using acoustic emissions (AE) for mechanical diagnostics, one major problem is the differentiation of events due to crack growth in a component from noise of various origins. This work presents two algorithms for automatic clustering and separation of AE events based on multiple features extracted from experimental data. The first algorithm consists of two steps. In the first step, the noise...

We discuss the reproducing kernel structure in shift-invariant spaces and the weighted shift-invariant spaces, and obtain the reconstruction formula in time-warped weighted shift-invariant spaces, then apply them to a spline subspace. In the spline subspace, we give a reconstruction formula in a time-warped spline subspace. 1. Introduction. The problem of reconstruction of a function f has been...

This paper discusses the problem of detecting subspace signals in structured interference plus compound Gaussian sea clutter with persymmetric structure. The is represented by a process wherein texture obeys inverse distribution. lies known subspace, which independent target signal subspace. By resorting to two-step generalized likelihood ratio test, Rao, and Wald design criteria, three adaptiv...

| In this paper, we investigate the noise suppression performance of a novel adaptive lter based on sub-space methods. In contrast to conventional subspace lters, this approach requires no eigenvalue or singular value decomposition. Therefore, our adaptive subspace lter can be implemented for real-time operation using general purpose digital signal processors. In addition, only the noisy signal...