Abstract
A gridless direction-of-arrival (DOA) estimation method to improve the estimation accuracy and resolution in nonuniform noise is proposed in this paper. This algorithm adopts the structure of minimum-redundancy linear array (MRA) and can be composed of two stages. In the first stage, by minimizing the rank of the covariance matrix of the true signal, the covariance matrix that filters out nonuniform noise is obtained, and then a gridless residual energy constraint scheme is designed to reconstruct the signal covariance matrix of the Hermitian Toeplitz structure. Finally, the unknown DOAs can be determined from the recovered covariance matrix, and the number of sources can be acquired as a byproduct. The proposed algorithm can be regarded as a gridless version method based on sparsity. Simulation results indicate that the proposed method has higher estimation accuracy and resolution compared with existing algorithms.
Highlights
Direction-of-arrival (DOA) estimation is one of the most important topics in the field of array signal processing [1]; its main purpose is to estimate the angle information of unknown signals spatially based on the array sensor model
In practical applications, the noise is usually considered to be nonuniform due to the misalignment of the antenna array or the nonidealities of the receiving channels. is nonuniformity leads to a decrease in the estimation accuracy of many DOA estimation algorithms based on the Gaussian white noise
He et al [6] proposed a DOA estimation algorithm based on the sparse representation of the covariance vector
Summary
Direction-of-arrival (DOA) estimation is one of the most important topics in the field of array signal processing [1]; its main purpose is to estimate the angle information of unknown signals spatially based on the array sensor model. Many measures have been taken to improve the performance of DOA estimation in nonuniform noise He et al [6] proposed a DOA estimation algorithm based on the sparse representation of the covariance vector. In order to reduce the estimation error and improve the estimation accuracy, a gridless residual constraint scheme that does not rely on noise parameters is designed, and the noiseless Toeplitz matrix is reconstructed. By operating on this matrix, we can obtain target angle information and get the number of sources as a byproduct. By operating on this matrix, we can obtain target angle information and get the number of sources as a byproduct. e proposed algorithm can be viewed as a gridless version of method based on sparsity, which overcomes the problem of basis mismatch in compressed sensing algorithms
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