An Ideal-Theoretic Criterion for Localization of an Unknown Number of Sources

Conference paper (2016)

Authors

M.W. Morency Signal Processing Systems - EEMCS
Sergiy A. Vorobyov Aalto University
G.J.T. Leus Signal Processing Systems - EEMCS
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2016 M.W. Morency, Sergiy A. Vorobyov, G.J.T. Leus
DOI
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https://doi.org/10.1109/ACSSC.2016.7869627
Clustering algorithms Estimation Generators Signal processing algorithms Eigenvalues and eigenfunctions Multiple signal classification Position measurement
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Published Date

01-11-2016

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

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.

Files

Asilomar16.pdf
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