Joint Detection and Localization of an Unknown Number of Sources Using Algebraic Structure of the Noise Subspace

Tutkimustuotos: Lehtiartikkeli

Tutkijat

Organisaatiot

  • Delft University of Technology

Kuvaus

Source localization and spectral estimation are among the most fundamental problems in statistical and array 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 these problems. 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 when not satisfied exactly, can potentially lead to severe errors. In this paper, we propose a new localization criterion based on the algebraic structure of the noise subspace that is described for the first time to the best of our knowledge. Using this criterion and the relationship between the source localization problem and the problem of computing the greatest common divisor (GCD), or more practically approximate GCD, for polynomials, we propose two algorithms which adaptively learn the number of sources and estimate their locations. Simulation results show a significant improvement over root-MUSIC in challenging scenarios such as closely located sources, both in terms of detection of the number of sources and their localization over a broad and practical range of SNRs. Further, no performance sacrifice in simple scenarios is observed.

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut4685 - 4700
Sivumäärä16
JulkaisuIEEE Transactions on Signal Processing
Vuosikerta66
Numero17
Varhainen verkossa julkaisun päivämäärä2018
TilaJulkaistu - 1 syyskuuta 2018
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

ID: 25909119