Chen, Po-Chih and Vaidyanathan, P. P. (2019) On ESPRIT with Multiple Coprime-Invariances. In: 2019 53rd Asilomar Conference on Signals, Systems, and Computers. IEEE , Piscataway, NJ, pp. 148-152. ISBN 9781728143002. https://resolver.caltech.edu/CaltechAUTHORS:20200402-144126560
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Abstract
In conventional ESPRIT, a single translational invariance in a sensor array is used to obtain high-resolution direction-of-arrival (DOA) estimation. However, when the invariance is greater than the classical sensor spacing λ/2, spatial frequency ambiguity may occur. In this paper, we propose to use multiple setwise coprime invariances to resolve this ambiguity. While special cases of this were known in the literature, our algorithm is more general in that we consider any number of invariances, and that it can perfectly recover any number of DOAs (limited only in terms of number of sensors) if infinite snapshots are available. We also demonstrate through simulation that our algorithm works well in a practical setting where only finite snapshots are available.
| Item Type: | Book Section | ||||||||
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| Additional Information: | © 2019 IEEE. This work was supported in parts by the NSF grant CCF-1712633, the ONR grant N00014-18-1-2390, and the California Institute of Technology. | ||||||||
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| Subject Keywords: | Linear sensor arrays, ESPRIT, multiple invariance, coprime invariance, pairing problem | ||||||||
| DOI: | 10.1109/ieeeconf44664.2019.9048797 | ||||||||
| Record Number: | CaltechAUTHORS:20200402-144126560 | ||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20200402-144126560 | ||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||
| ID Code: | 102278 | ||||||||
| Collection: | CaltechAUTHORS | ||||||||
| Deposited By: | Tony Diaz | ||||||||
| Deposited On: | 02 Apr 2020 22:20 | ||||||||
| Last Modified: | 16 Nov 2021 18:10 |
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