An ideal-theoretic criterion for localization of an unknown number of sources

Matthew W. Morency, Sergiy Vorobyov, G. Leus

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)


Source localization is among the most fundamental problems in statistical signal processing. Methods which rely on the orthogonality of the signal and noise subspaces, such as Pisarenko's method, MUSIC, and root-MUSIC are some of the most widely used algorithms to solve this problem. As a common feature, these methods require both a-priori knowledge of the number of sources, and an estimate of the noise subspace. Both requirements are complicating factors to the practical implementation of the algorithms, and sources of potentially severe error. In this paper, we propose a new localization criterion based on the algebraic structure of the noise subspace. An algorithm is proposed which adaptively learns the number of sources and estimates their locations. Simulation results show significant improvement over root-MUSIC, even when the correct number of sources is provided to the root-MUSIC algorithm.
Original languageEnglish
Title of host publicationAn ideal-theoretic criterion for localization of an unknown number of sources
Number of pages4
ISBN (Electronic)978-1-5386-3954-2
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventAsilomar Conference on Signals, Systems & Computers - Pasific Grove, United States
Duration: 6 Nov 20169 Nov 2016
Conference number: 50

Publication series

NameConference Record of the Asilomar Conference on Signals Systems and Computers
ISSN (Print)1058-6393


ConferenceAsilomar Conference on Signals, Systems & Computers
Abbreviated titleASILOMAR
Country/TerritoryUnited States
CityPasific Grove
Internet address


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