Documents

  • Asilomar16

    Accepted author manuscript, 218 KB, PDF-document

DOI

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 publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1499-1502
Number of pages4
ISBN (Electronic)978-1-5386-3954-2
DOIs
Publication statusPublished - 1 Nov 2016
Event50th Asilomar ConFerence on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: 6 May 20169 May 2016
http://www.asilomarsscconf.org/

Conference

Conference50th Asilomar ConFerence on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period6/05/169/05/16
Internet address

    Research areas

  • Eigenvalues and eigenfunctions, Multiple signal classification, Signal processing algorithms, Generators, Position measurement, Clustering algorithms, Estimation

ID: 11231880