Two-Channel Matched Subspace Detectors

David Ramírez, Ignacio Santamaría and Louis Scharf
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David Ramírez: Universidad Carlos III de Madrid
Ignacio Santamaría: Universidad de Cantabria
Louis Scharf: Colorado State University

Chapter 7 in Coherence, 2022, pp 203-234 from Springer

Abstract: Abstract This chapter considers the detection of a common subspace signal in two multi-sensor channels. This problem is usually referred to as passive detection. We study second-order detectors where the unknown transmitted signal is modeled as a zero-mean Gaussian and averaged out or marginalized and first-order detectors where the unknown transmitted signal appears in the mean of the observations with no prior distribution assigned to it. The signal subspaces at the two sensor arrays may be known or unknown but with known dimension. In the first case, the resulting detectors are termed matched subspace detectors; in the second case, they are matched direction detectors. We study different noise models ranging from spatially white noises with identical variances to arbitrarily correlated Gaussian noises. For each noise and signal model, the invariances of the hypothesis testing problem and its GLR are established. Maximum likelihood estimation of unknown signal and noise parameters leads to a variety of coherence statistics.

Keywords: Generalized likelihood ratio; Passive detection; Passive radar; Matched subspace detector; Matched direction detector; Alternating optimization (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-13331-2_7

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DOI: 10.1007/978-3-031-13331-2_7

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