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Difference Coarray-Based Direction Finding via Covariance Fitting
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DOI：10.1109/LCOMM.2021.3078877.png)
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摘要
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Sparse linear arrays like nested arrays and coprime arrays, can offer more accurate direction-of-arrival estimation than uniform linear arrays by exploiting their difference coarrays. In this letter, we present a new algorithm for direction finding using difference coarray. The proposed estimator firstly formulates a recovery problem of a column vector based on the covariance fitting criterion and the probability distribution of the vectorized estimation error in the coarray covariance matrix. Subsequently, we reconstruct the expected coarray covariance matrix by recovering the column vector. Based on the reconstructed covariance matrix, the MUSIC algorithm is adopted to perform direction finding. Numerical results demonstrate the superiority of our scheme over several existing approaches.
Keyword:
Covariance matrices
Multiple signal classification
Signal processing algorithms
Sensors
Sensor arrays
Direction-of-arrival estimation
Probability distribution
Direction finding
difference coarray
covariance fitting
coarray covariance matrix
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期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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Sparsity-Based Direction Finding of Coherent and Uncorrelated Targets Using Active Nonuniform Arrays
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