Solving ambiguity for sparse array via particle swarm optimization

Sparse linear arrays are subject to manifold ambiguity in genera. A method to solve the manifold ambiguity of uncorrelated sources for sparse array is proposed in this paper. The method is consisted of two steps. The first step is to obtain all the directions of arrivals (DOAs) by traditional MUltip...

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Vydané v:2011 International Conference on Computational Problem-Solving s. 316 - 319
Hlavní autori: Ziyuan He, Zhiqin Zhao, Kai Yang, Jun Ouyang
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.10.2011
Predmet:
Arrays
Direction of arrival estimation
Estimation
Manifolds
Multiple signal classification
Signal processing algorithms
ISBN:9781457706028, 1457706024
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Shrnutí:Sparse linear arrays are subject to manifold ambiguity in genera. A method to solve the manifold ambiguity of uncorrelated sources for sparse array is proposed in this paper. The method is consisted of two steps. The first step is to obtain all the directions of arrivals (DOAs) by traditional MUltiple SIgnal Classification (MUSIC) algorithm, including true and spurious DOAs. The second step is to estimate the power values of all the DOAs by substituting all the DOAs to a cost function. The particle warm optimization (PSO) are applied to estimate the power values. The power values of spurious DOAs are very small or tends to zero compared with the values of the true DOAs. Simulation results demonstrate the effectiveness and the feasibility of the method.
ISBN:9781457706028
1457706024
DOI:10.1109/ICCPS.2011.6092279