A Unified Theory of Adaptive Subspace Detection Part I: Detector Designs

This paper addresses the problem of detecting multidimensional subspace signals, which model range-spread targets, in noise unknown covariance. It is assumed that a primary channel measurements, possibly consisting signal plus noise, augmented with secondary measurements containing only noise. The noises these two channels share common covariance matrix, up to scale, may be known or unknown. variations: by its dimension; consecutive visits unconstrained they constrained prior distribution. As consequence, there are four general classes detectors and, within each class, detector for case where scale between and known, this generalized likelihood ratio (GLR) based derived paper, when organized previously published GLR detectors, comprise unified theory adaptive detection from measurements.