Matched Subspace Detectors

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

Chapter 5 in Coherence, 2022, pp 149-183 from Springer

Abstract: Abstract This chapter is devoted to the detection of signals that are constrained to lie in a subspace. The subspace may be known, or known only by its dimension. The probability distribution for the measurements may carry the signal in a parameterization of the mean or in a parameterization of the covariance matrix. Likelihood ratio detectors are derived, their invariances are revealed, and their null distributions are derived where tractable. The result is a comprehensive account of matched subspace detectors in the multivariate normal model.

Keywords: Coherence; Generalized likelihood ratio; Matched subspace detector; Matched direction detector (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-13331-2_5

Ordering information: This item can be ordered from
http://www.springer.com/9783031133312

DOI: 10.1007/978-3-031-13331-2_5

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().