2D DOA Estimation Using Sparse Representation of Higher-Order Power of Covariance Matrix

Abstract:

A novel two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm utilizing a sparse signal representation of higher-order power of covariance matrix is proposed. Through applying the higher-order power of covariance matrix to construct a new sparse decomposition vector, this algorithm avoids the estimation of incident signal number and eigenvalue decomposition. And the hierarchical granularity-dictionary is studied, which forms the over-complete dictionary adaptively in the light of source signals’ distribution. Compared with MUSIC and L1-SVD, this algorithm not only provides a better 2D DOA performance but also possesses the capability of coherent signals estimation. Theoretical analysis and simulation results demonstrate the validity and robust of the proposed algorithm.

• Periodical: Advanced Materials Research (Volumes 998-999)
• Pages: 779-783
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.998-999.779
• Online since: July 2014
• Authors: Zheng Luo*, Fei Yu, Lin Wu, Yuan Liu
• Keywords: Covariance Matrix, Granularity Uniform, Sparse Decomposition, Two-Dimensional Direction of Arrival Estimation

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