平移嵌套陣列稀疏貝葉斯學(xué)習(xí)角度估計(jì)算法

陳璐; 畢大平; 潘繼飛

doi:10.11999/JEIT170737

平移嵌套陣列稀疏貝葉斯學(xué)習(xí)角度估計(jì)算法

doi: 10.11999/JEIT170737

2.
(國(guó)防科技大學(xué)電子對(duì)抗學(xué)院合肥 230037) ②(安徽省電子制約技術(shù)重點(diǎn)實(shí)驗(yàn)室合肥 230037)

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61671453)，安徽省自然科學(xué)基金(1608085MF123)

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- 文章訪問(wèn)數(shù): 1266
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出版歷程
- 收稿日期: 2017-07-20
- 修回日期: 2018-01-30
- 刊出日期: 2018-05-19

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

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)

摘要

摘要: 針對(duì)陣元間互耦效應(yīng)導(dǎo)致嵌套陣列測(cè)向性能下降的問(wèn)題，該文提出兩種不同的平移嵌套陣列結(jié)構(gòu)，在保證產(chǎn)生虛擬陣列無(wú)孔的條件下，通過(guò)對(duì)原二級(jí)嵌套陣列陣元位置進(jìn)行調(diào)整，形成平移嵌套陣列，提高了原二級(jí)嵌套陣列的稀疏性，降低了陣元間的互耦效應(yīng)，擴(kuò)展了原嵌套陣列的測(cè)向自由度。在空間輻射源數(shù)目未知條件下，建立了平移嵌套陣列稀疏貝葉斯學(xué)習(xí)(SBL)算法模型，對(duì)形成的虛擬陣列接收數(shù)據(jù)進(jìn)行處理，獲得角度估計(jì)，有效提高了原嵌套陣列測(cè)向算法的測(cè)向性能。仿真實(shí)驗(yàn)表明，平移嵌套陣列自由度高于原嵌套陣列，在低信噪比、小快拍數(shù)、存在互耦影響條件下，基于稀疏貝葉斯學(xué)習(xí)的平移嵌套陣列測(cè)向算法測(cè)向精度優(yōu)于原嵌套陣列測(cè)向算法，并且提高了原嵌套陣列測(cè)向算法的角度分辨率。
- 輻射源角度估計(jì) /
- 嵌套陣列 /
- 壓縮感知 /
- 互耦效應(yīng) /
- 稀疏貝葉斯學(xué)習(xí)
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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參考文獻(xiàn)(20)

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