平移嵌套阵列稀疏贝叶斯学习角度估计算法

陈璐; 毕大平; 潘继飞

doi:10.11999/JEIT170737

平移嵌套阵列稀疏贝叶斯学习角度估计算法

doi: 10.11999/JEIT170737

2.
(国防科技大学电子对抗学院合肥 230037) ②(安徽省电子制约技术重点实验室合肥 230037)

基金项目:

国家自然科学基金(61671453)，安徽省自然科学基金(1608085MF123)

计量
- 文章访问数: 1241
- HTML全文浏览量: 214
- PDF下载量: 148
- 被引次数: 0
出版历程
- 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: 针对阵元间互耦效应导致嵌套阵列测向性能下降的问题，该文提出两种不同的平移嵌套阵列结构，在保证产生虚拟阵列无孔的条件下，通过对原二级嵌套阵列阵元位置进行调整，形成平移嵌套阵列，提高了原二级嵌套阵列的稀疏性，降低了阵元间的互耦效应，扩展了原嵌套阵列的测向自由度。在空间辐射源数目未知条件下，建立了平移嵌套阵列稀疏贝叶斯学习(SBL)算法模型，对形成的虚拟阵列接收数据进行处理，获得角度估计，有效提高了原嵌套阵列测向算法的测向性能。仿真实验表明，平移嵌套阵列自由度高于原嵌套阵列，在低信噪比、小快拍数、存在互耦影响条件下，基于稀疏贝叶斯学习的平移嵌套阵列测向算法测向精度优于原嵌套阵列测向算法，并且提高了原嵌套阵列测向算法的角度分辨率。
- 辐射源角度估计 /
- 嵌套阵列 /
- 压缩感知 /
- 互耦效应 /
- 稀疏贝叶斯学习
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)

HTML全文

参考文献(20)

PAL P and VAIDYANATHAN P P. Nested arrays: A novel approach to array processing with enhanced degrees of freedom[J]. IEEE Transactions on Signal Processing, 2010, 58(8): 4167-4181. doi: 10.1109/TSP.2010.2049264.

PAL P and VAIDYANATHAN P P. Nested arrays in two dimensions, part I: Geometrical considerations[J]. IEEE Transactions on Signal Processing, 2012, 60(9): 4694-4705. doi: 10.1109/TSP.2012.2203814.

HAN K Y and NEHORAI A. Nested array processing for distributed sources[J]. IEEE Signal Processing Letters, 2014, 21(9): 1111-1114. doi: 10.1109/LSP.2014.2325000.

HAN K Y and NEHORAI A. Improved source number detection and direction estimation with nested arrays and ULAs using jackknifing[J]. IEEE Transactions on Signal Processing, 2013, 61(23): 6118-6128. doi: 10.1109/TSP.2013. 2283462.

LIU C L and VAIDYANATHAN P P. Hourglass arrays and other novel 2-D sparse arrays with reduced mutual coupling[J]. IEEE Transactions on Signal Processing, 2017, 65(13): 3369-3383. doi: 10.1109/TSP.2017.2690390.

ROCCA P, HAN M, SALUCCI M, et al. Single-snapshot DOA estimation in array antennas with mutual coupling through a multi-scaling Bayesian compressive sensing strategy[J]. IEEE Transactions on Antennas and Propagation, 2017, 65(6): 3203-3213. doi: 10.1109/TAP.2017.2684137.

AKSOY T and TUNCER T E. Measurement reduction for mutual coupling calibration in DOA estimation[J]. Radio Science, 2012, 47(3): 1-9. doi: 10.1029/2011RS004904.

DAI J, BAO X, HU N, et al. A recursive rare algorithm for DOA estimation with unknown mutual coupling[J]. IEEE Antennas and Wireless Propagation Letters, 2014, 13: 1593-1596. doi: 10.1109/LAWP.2014.2347056.

MAO W, LI G, XIE X, et al. DOA estimation of coherent signals based on direct data domain under unknown mutual coupling[J]. IEEE Antennas and Wireless Propagation Letters, 2014, 13: 1525-1528. doi: 10.1109/LAWP.2014.2343671.

DAI J, ZHAO D, and JI X. A sparse representation method for DOA estimation with unknown mutual coupling[J]. IEEE Antennas and Wireless Propagation Letters, 2012, 11: 1210-1213. doi: 10.1109/LAWP.2012.2223651.

LIAO B, ZHANG Z G, and CHAN S C. DOA estimation and tracking of ULAs with mutual coupling[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1): 891-905. doi: 10.1109/TAES.2012.6129676.

LIU C L and VAIDYANATHAN P P. Super nested arrays: Linear sparse arrays with reduced mutual couplingPart I: Fundamentals[J]. IEEE Transactions on Signal Processing, 2016, 64(15): 3997-4012. doi: 10.1109/TSP.2016.25581592015.

QIAN C, HUANG L, and SO H C. Improved unitary root- MUSIC for DOA estimation based on pseudo-noise resampling[J]. IEEE Signal Processing Letters, 2014, 21(2): 140-144. doi: 10.1109/LSP.2013.2294676.

YAN F, JIN M, and QIAO X. Low-complexity DOA estimation based on compressed MUSIC and its performance analysis[J]. IEEE Transactions on Signal Processing, 2013, 61(8): 1915-1930. doi: 10.1109/TSP.2013.2243442.

LIN J, MA X, YAN S, et al. Time-frequency multi-invariance ESPRIT for DOA estimation[J]. IEEE Antennas and Wireless Propagation Letters, 2016, 15: 770-773. doi: 10.1109 /LAWP.2015.2473664.

ABRAMOVICH Y I and JOHNSON B A. Detection- estimation of very close emitters: Performance breakdown, ambiguity, and general statistical analysis of maximum- likelihood estimation[J]. IEEE Transactions on Signal Processing, 2010, 58(7): 3647-3660. doi: 10.1109/TSP.2010. 2047334.

FANG J, LI J, SHEN Y, et al. Super-resolution compressed sensing: An iterative reweighted algorithm for joint parameter learning and sparse signal recovery[J]. IEEE Signal Processing Letters, 2014, 21(6): 761-765. doi: 10.1109/LSP.2014.2316004.

.[18] HU R, FU Y, CHEN Z, et al. Robust DOA estimation via sparse signal reconstruction with impulsive noise[J]. IEEE Communications Letters, 2017, 21(6): 1333-1336. doi: 10.1109/LCOMM.2017.2675407.

HAWES M, MIHAYLOVA L, SEPTIER F, et al. Bayesian compressive sensing approaches for direction of arrival estimation with mutual coupling effects[J]. IEEE Transactions on Antennas and Propagation, 2017, 65(3): 1357-1368. doi: 10.1109/TAP.2017.2655013.

YANG X, CHI C K, and ZHENG Z. Direction-of-arrival estimation of incoherently distributed sources using Bayesian compressive sensing[J]. IET Radar, Sonar Navigation, 2016, 10(6): 1057-1064. doi: 10.1049/iet-rsn.2015.0336.

施引文献

资源附件(0)

访问统计