基于协方差矩阵高阶幂的二维DOA估计新算法

doi:CNKI:11-1929/V.20111209.1725.004

Abstract

Abstract: To reduce the computational complexity in the sparse decomposition of 2D direction of arrival (DOA) estimation based on uniform circular arrays (UCAs), a novel 2D DOA estimation algorithm using sparse decomposition of higher-order power of covariance matrix was proposed. First, this method avoids the estimation of the number of signals and eigen-value decompsition through using the high-order power of a covariance matrix as the vector of sparse decomposition. Then a fast region direction detection decomposition algorithm based on the hierarchical granularity model is put forward. This new method can construct a redundant dictionary adaptively based on the distribution of space signals, thus reducing the computational load greatly while still maintaining high estimation accuracy. Compared with the 2D MUSIC method,this algorithm not only provides better 2D DOA performance but also possesses the capability of estimating coherent signals. Simulation results confirm its effectiveness and feasibility.

Key words: uniform circular array, two-dimensional direction of arrival estimation, sparse decomposition, covariance matrix, high order power, granularity

CLC Number:

LUO Zheng, ZHANG Min, LI Pengfei. A Novel 2D DOA Estimation Algorithm Based on High-order Power of Covariance Matrix[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, (4): 696-704.

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A Novel 2D DOA Estimation Algorithm Based on High-order Power of Covariance Matrix

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