Matched subspace detectors

Matched subspace detectors

In this paper we formulate a general class of problems for detecting subspace signals in subspace interference and broadband noise. We derive the generalized likelihood ratio (GLR) for each problem in the class. We then establish the invariances for the GLR and argue that these are the natural invariances for the problem. In each case, the GLR is a maximal invariant statistic, and the distribut...

Matched Subspace Detectors for Stochastic Signals

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...

Matched subspace detectors and distributed detection

Adaptive matched subspace detectors for hyperspectral imaging applications

Real-time detection and identification of man-made objects or materials (“targets”) from airborne platforms using hyperspectral sensors are of great interest for civilian and military applications. Over the past several years, different algorithms for the detection of targets with known spectral signature have been developed. Most of these algorithms have been reviewed in [1] within a unified t...

Tensor Matched Subspace Detection

The problem of testing whether an incomplete tensor lies in a given tensor subspace is significant when it is impossible to observe all entries of a tensor. We focus on this problem under tubal-sampling and elementwise-sampling. Different from the matrix case, our problem is much more challenging due to the curse of dimensionality and different definitions for tensor operators. In this paper, t...