Sparse covariance fitting method for direction of arrival estimation of uncorrelated wideband signals

Pejoski, Slavche; Kafedziski, Venceslav

doi:10.5937/telfor1402115p

Cited by 3 publications

(1 citation statement)

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“…The target signals can be regarded to be sparse in a spatial domain, and their DOAs can be estimated according to the array received data and a redundant dictionary. The DOA estimation algorithm by utilizing the idea of sparse representation mainly divides into two kinds [ 3 , 4 ]. One is the sparse model based on the array received data, and the other one is the sparse model based on the array covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

The Real-Valued Sparse Direction of Arrival (DOA) Estimation Based on the Khatri-Rao Product

Chen

Zhao

2016

Sensors

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There is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA) of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV) model is difficult. In this paper, a real-valued sparse DOA estimation algorithm based on the Khatri-Rao (KR) product called the L1-RVSKR is proposed. The proposed algorithm is based on the sparse representation of the array covariance matrix. The array covariance matrix is transformed to a real-valued matrix via a unitary transformation so that a real-valued sparse model is achieved. The real-valued sparse model is vectorized for transforming to a single measurement vector (SMV) model, and a new virtual overcomplete dictionary is constructed according to the KR product’s property. Finally, the sparse DOA estimation is solved by utilizing the idea of a sparse representation of array covariance vectors (SRACV). The simulation results demonstrate the superior performance and the low computational complexity of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%