Abstract
This paper proposes a direction of arrival estimation based on sparse signal reconstruction in the presence of alpha noise by the off-grid orthogonal matching pursuit algorithm. Assuming Gaussian distribution as the noise model, the previous sparse reconstruction enhances robustness by utilizing the least square criterion-based direction of arrival estimation algorithms. However, they severely degrade, even invalid, when there is thick trailing and large impulse in the noise molded by [Formula: see text] stable distribution. In addition, due to the discretization of the potential angle space, the accuracy of these methods will be reduced when the target is not completely on the divided mesh exactly. Increasing the grid density to improve estimation effect will increase the computation burden. The compressed sensing signal model is reconstructed by reshaping the fractional lower order covariance matrix of the sensor array received signal. Based on the reshaped signal model, the novel reshaped orthogonal matching pursuit algorithm and reshaped off-grid orthogonal matching pursuit algorithm are derived. Compared to the least square criterion method with Gaussian distribution assumption, the proposed algorithm obtains high-resolution direction of arrival estimation in [Formula: see text] noise. Moreover, the reshaped off-grid orthogonal matching pursuit algorithm improves the direction of arrival estimation accuracy with off-grid target. Numerical simulation results demonstrate the effectiveness of proposed method in direction of arrival estimation with off-grid targets in [Formula: see text] noise.
Highlights
Direction of arrival (DOA) estimation of multi-source signals is an important issue in the field of array signal processing, which is widely used in radar, sonar, wireless communications, and other fields
When the statistical properties of Gaussian noise are known, methods based on second-order moments and other algorithms based on the least square (LS) criterion can be used
The Compressed Sensing (CS) signal model is reconstructed by reshaping the fractional lower order covariance (FLOC) matrix based on KR subspace approach and devises two OMP algorithms, namely reshaped orthogonal matching pursuit (R-OMP) and reshaped off-grid orthogonal matching pursuit (R-OGOMP), to address DOA estimation problem with a noise
Summary
Direction of arrival (DOA) estimation of multi-source signals is an important issue in the field of array signal processing, which is widely used in radar, sonar, wireless communications, and other fields. When the statistical properties of Gaussian noise are known, methods based on second-order moments and other algorithms based on the least square (LS) criterion can be used. The DOA estimation approach on spatial smoothing for coherent signals in impulsive noise is proposed in Li and Lin.[12] the subspace-based algorithms are computationally expensive. In order to improve the accuracy, Ma et al.[20] suppress Gaussian noise by second-order statistics and developed Khatri–Rao (KR) subspace approach, Li et al.[21] suppresses a noise by fractional lower order statistics and reconstructed the CS signal model based on KR subspace approach. The CS signal model is reconstructed by reshaping the fractional lower order covariance (FLOC) matrix based on KR subspace approach and devises two OMP algorithms, namely reshaped orthogonal matching pursuit (R-OMP) and reshaped off-grid orthogonal matching pursuit (R-OGOMP), to address DOA estimation problem with a noise. The LS criterion method of sparse signal reconstruction in CS is invalid all the same in a noise
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