A Direction of Arrial Estimation Algorithm for Translational Nested Array Besed on Sparse Bayesian Learning

CHEN Lu; BI Daping; PAN Jifei

doi:10.11999/JEIT170737

Volume 40 Issue 5

May 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(5): 1173-1180

CHEN Lu, BI Daping, PAN Jifei. A Direction of Arrial Estimation Algorithm for Translational Nested Array Besed on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1173-1180. doi: 10.11999/JEIT170737

Citation:

CHEN Lu, BI Daping, PAN Jifei. A Direction of Arrial Estimation Algorithm for Translational Nested Array Besed on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1173-1180. doi: 10.11999/JEIT170737

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A Direction of Arrial Estimation Algorithm for Translational Nested Array Besed on Sparse Bayesian Learning

doi: 10.11999/JEIT170737

1.
(College of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, China)
2.
(College of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, China)

Funds:

The National Natural Science Foundation of China (61671453), The Natural Science Foundation of Anhui Province (1608085MF123)

Received Date: 2017-07-20
Rev Recd Date: 2018-01-30
Publish Date: 2018-05-19

Abstract

Abstract

The performance of direction finding for nested array degrades due to the mutual coupling effect among the elements. Two different translational nested array structures are proposed. In order to ensure that the virtual array has no holes, a translational nested array is formed by adjusting the positions of the original two level nested array elements. It improves the sparsity of the original two level nested array, reduces the mutual coupling effect, and extends the direction finding freedom of the original nested array. Under the condition of unknown number of spatial radiation sources, a Sparse Bayesian Learning (SBL) model for translational nested array is established. Through this model, the received data of the virtual array is processed, the DOA estimation is obtained and the direction finding performance of the original nested array direction finding algorithm is effectively improved. Simulation results show that the translational nested array has higher degree of freedom than the original nested array. Under the scenarios of low Signal-to-Noise Ratio (SNR), snapshot deficiency, and mutual coupling effect, the performance of direction finding algorithm for translational nested array based on Sparse Bayesian Learning is better than that of direction finding algorithm for the original nested array. The angle resolution of direction finding algorithm for the original nested array is improved.
- DOA estimation,
- Nested array,
- Compressed Sensing (CS),
- Mutual coupling effects,
- Sparse Bayesian Learning (SBL)

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References(20)

References

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