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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Bibliographic Details
Published in:2011 International Conference on Computational Problem-Solving pp. 316 - 319
Main Authors: Ziyuan He, Zhiqin Zhao, Kai Yang, Jun Ouyang
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2011
Subjects:
Arrays
Direction of arrival estimation
Estimation
Manifolds
Multiple signal classification
Signal processing algorithms
ISBN:9781457706028, 1457706024
Online Access:Get full text
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Summary: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