Method of solving ambiguity for sparse array via power estimation based on MUSIC algorithm

Authors:
Ziyuan He;Zhiqin Zhao;Zaiping Nie;Pu Tang;Jian Wang;Qing-Huo Liu
Affiliations:
School of Electronic Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, PR China;School of Electronic Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, PR China;School of Electronic Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, PR China;School of Electronic Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, PR China;School of Electronic Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, PR China;Department of Electrical Engineering, Duke University, Durham, NC, USA
Venue:
Signal Processing
Year:
2012

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Abstract

Sparse linear arrays provide better performance than the filled linear arrays in terms of direction estimation and resolution with reduced size and low cost. However, they are subject to manifold ambiguity. A method based on the Multiple Signal Classification (MUSIC) algorithm to solve the manifold ambiguity of uncorrelated sources for sparse array is proposed in this paper. The method consists of two steps. The first step is to obtain all the directions of arrivals (DOAs), including true and spurious DOAs, using traditional MUSIC. The second step is to estimate the power values of the all DOAs by substituting all the DOAs to a cost function. The well-known Davidson Fletcher Powell (DFP) and Broyden Fletcher Goldfarb Shanno (BFGS) algorithms are used to estimate the power values. The power values of spurious DOAs are very small or tend to zero compared with the values of the true DOAs. The true DOAs are then discriminated easily from the spurious DOAs with the power values. Simulation results demonstrate the effectiveness and the feasibility of the method.