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Adaptive detection of a signal known only to lie on a line in a known subspace, when primary and secondary data are partially homogeneous

Besson, Olivier and Scharf, Louis and Kraut, Shawn Adaptive detection of a signal known only to lie on a line in a known subspace, when primary and secondary data are partially homogeneous. (2006) IEEE Transactions on Signal Processing, vol. 5 (n° 12). pp. 4698-4705. ISSN 1053-587X

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Official URL: http://dx.doi.org/10.1109/TSP.2006.881262

Abstract

This paper deals with the problem of detecting a signal, known only to lie on a line in a subspace, in the presence of unknown noise, using multiple snapshots in the primary data. To account for uncertainties about a signal's signature, we assume that the steering vector belongs to a known linear subspace. Furthermore, we consider the partially homogeneous case, for which the covariance matrix of the primary and the secondary data have the same structure but possibly different levels. This provides an extension to the framework considered by Bose and Steinhardt. The natural invariances of the detection problem are studied, which leads to the derivation of the maximal invariant. Then, a detector is proposed that proceeds in two steps. First, assuming that the noise covariance matrix is known, the generalized-likelihood ratio test (GLRT) is formulated. Then, the noise covariance matrix is replaced by its sample estimate based on the secondary data to yield the final detector. The latter is compared with a similar detector that assumes the steering vector to be known.

Item Type:Article
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Other partners > Colorado State University - CSU (USA)
Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE
Other partners > Massachusetts Institute of Technology - MIT (USA)
Laboratory name:
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Deposited By: Olivier Besson
Deposited On:27 Mar 2008 14:06

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