Name
Abstract | ||
|---|---|---|
Sparse arrays such as co-prime and nested arrays can identify more sources than the number of sensors. This is because their difference co-arrays contain a uniformly spaced virtual array with more elements than the number of sensors in the array. In this paper we demonstrate this using two dimensional co-prime and nested sparse arrays combined with sparse Bayesian learning (SBL) for 2D beamforming in azimuth and elevation. SBL can directly process the sparse array data and significantly outperform conventional beam forming and MUSIC as seen from simulations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8682747 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
2D beamforming, SBL, co-prime arrays, nested arrays, compressive sensing | Beamforming,Sparse array,Bayesian inference,Pattern recognition,Computer science,Signal-to-noise ratio,Azimuth,Artificial intelligence,Elevation,Nested arrays,Virtual array | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Santosh Nannuru | 1 | 66 | 6.57 |
| Peter Gerstoft | 2 | 86 | 22.34 |