NDLI: Matched and adaptive subspace detectors when interference dominates noise

Content Provider	IEEE Xplore Digital Library
Author	Scharf, L.L. McCloud, M.L.
Copyright Year	2000
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Colorado Univ., Boulder, CO, USA (Scharf, L.L.)
Abstract	In much of modern radar, sonar and wireless communication it seems more reasonable to model measurements as signal-plus-subspace interference-plus-broadband noise, than as signal-plus-colored noise. This observation leads naturally to a variety of detection and estimation problems in the linear statistical model. To solve these problems, one requires oblique pseudo-inverses, oblique projections, and zero-forcing orthogonal projections. The problem is that these operators depend on knowledge of signal and interference subspaces, and this information is often not at hand. More typically the signal subspace is known, but the interference subspace is unknown. In this paper we prove a theorem which allows these operators to be estimated directly from experimental data, without knowledge of the interference subspace. As a by-product, the theorem shows how signal subspace covariance and power may be estimated. The results of this paper form the foundation for the rapid adaptation of receivers which are then used for detection and estimation. They may be applied to detection and estimation in radar and sonar and to data decoding in multiuser communication receivers.
Starting Page	1105
Ending Page	1108
File Size	323086
Page Count	4
File Format	PDF
ISBN	0780365143
ISSN	10586393
DOI	10.1109/ACSSC.2000.910686
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-10-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Interference Radar detection Sonar detection Radar measurements Sonar measurements Wireless communication Noise measurement Sonar applications Decoding
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Networks and Communications

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