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Source localization using recursively applied and projected (RAP) MUSIC
Abstract
A new method for source localization is described that is based on a modification of the well-known MUSIC algorithm. In classical MUSIC, the array manifold vector is projected onto an estimate of the signal subspace. Errors in the estimate of the signal subspace can make localization of multiple sources difficult. Recursively applied and projected (RAP) MUSIC uses each successively located source to form an intermediate array gain matrix and projects both the array manifold and the signal subspace estimate into its orthogonal complement. The MUSIC projection to find the next source is then performed in this reduced subspace. Special assumptions about the array manifold structure, such as Vandermonde or shift invariance, are not required. Using the metric of principal angles, we describe a general form of the RAP-MUSIC algorithm for the case of diversely polarized sources. Through a uniform linear array simulation with two highly correlated sources, we demonstrate the improved Monte Carlo error performance of RAP-MUSIC relative to MUSIC and two other sequential subspace methods: S and IES-MUSIC. We then demonstrate the more general utility of this algorithm for multidimensional array manifolds in a magnetoencephalography (MEG) source localization simulation.
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Metadata
- Authors
- Mosher, J. C., & Leahy, R. M.
- Affiliations
- Los Alamos National Laboratory (US)
- University of Southern California (US)
- Venue
- IEEE Transactions on Signal Processing
- Published
- 1999
- Volume
- 47
- Issue
- 2
- Pages
- 332-340
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2126767504
- Cited by
- 398
- References
- 16
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 11 · 2023: 15 · 2022: 18 · 2021: 19 · 2020: 17 · 2019: 16 · 2018: 20 · 2017: 24 · 2016: 8 · 2015: 19 · 2014: 16 · 2013: 15 · 2012: 17
- FWCI
- 11.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Signal Processing (publishedVersion)
- Funders
- University of Southern California
- Villanova University
- Topics
- Physical Sciences › Computer Science › Signal Processing › Direction-of-Arrival Estimation Techniques
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- source localization, rap music, MUSIC algorithm, signal subspace estimation, principal angles, correlated sources, uniform linear array, magnetoencephalography
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