Abstract
Direction of arrival algorithms which exploit the eigenstructure of the spatial covariance matrix (such as MUSIC) encounter difficulties in the presence of strongly correlated sources. Since the broadband polynomial MUSIC is an extension of the narrowband version, it is unsurprising that the same issues arise. In this paper, we extend the spatial smoothing technique to broadband scenarios via spatially averaging polynomial spacetime covariance matrices. This is shown to restore the rank of the polynomial source covariance matrix. In the application of the polynomial MUSIC algorithm, the spatially smoothed spacetime covariance matrix greatly enhances the direction of arrival estimate in the presence of strongly correlated sources. Simulation results are described shows the performance improvement gained using the new approach compared to the conventional non-smoothed method.
Original language | English |
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Title of host publication | 25th European Signal Processing Conference, EUSIPCO 2017 |
Place of Publication | Piscataway, NJ. |
Publisher | IEEE |
Pages | 2448-2452 |
Number of pages | 5 |
Volume | 2017-January |
ISBN (Electronic) | 9780992862671 |
DOIs | |
Publication status | Published - 23 Oct 2017 |
Event | 25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece Duration: 28 Aug 2017 → 2 Sept 2017 |
Conference
Conference | 25th European Signal Processing Conference, EUSIPCO 2017 |
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Country/Territory | Greece |
City | Kos |
Period | 28/08/17 → 2/09/17 |
Keywords
- spatial smoothing
- direction of arrival
- DoA
- polynomial eigenvalue decomposition
- PEVD