Direction-of-Arrival Estimation of Near-Field Sources Based on Two Symmetric Nested Arrays with Enhanced Degrees of Freedom

2018 ◽  
pp. 222-233
Author(s):  
Shuang Li ◽  
Shunren Hu ◽  
Wei Liu ◽  
Xiaoxiao Jiang ◽  
Wei He
Keyword(s):  
Degrees Of Freedom ◽  
Near Field ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Nested Arrays

Direction-of-Arrival Estimation for Near-Field Sources with Multiple Symmetric Subarrays

2013 ◽  
Vol E96.B (2) ◽  
pp. 553-560
Author(s):  
Tomoyuki KITADA ◽  
Jun CHENG ◽  
Yoichiro WATANABE
Keyword(s):  
Near Field ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation

Direction of Arrival Estimation Under Near-Field Interference Using Matrix Filter

2015 ◽  
Vol 23 (04) ◽  
pp. 1540007 ◽  
Author(s):  
Guolong Liang ◽  
Wenbin Zhao ◽  
Zhan Fan
Keyword(s):  
Near Field ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Far Field ◽  
Data Simulation ◽  
Estimation Algorithms ◽  
Simulation Results

Direction of arrival (DOA) estimation is of great interest due to its wide applications in sonar, radar and many other areas. However, the near-field interference is always presented in the received data, which may result in degradation of DOA estimation. An approach which can suppress the near-field interference and preserve the far-field signal desired by using a spatial matrix filter is proposed in this paper and some typical DOA estimation algorithms are adjusted to match the filtered data. Simulation results show that the approach can improve capability of DOA estimation under near-field inference efficiently.

scholarly journals Direction-of-Arrival Estimation of Far-Field Sources Under Near-Field Interferences in Passive Sonar Array

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 28413-28420
Author(s):  
Hojun Lee ◽  
Jongmin Ahn ◽  
Yongcheol Kim ◽  
Jaehak Chung
Keyword(s):  
Near Field ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Far Field ◽  
Passive Sonar

Direction of arrival estimation of near‐field sources using sparse representation framework.

2011 ◽  
Vol 129 (4) ◽  
pp. 2664-2664
Author(s):  
Yinghui Zhao ◽  
Mahmood R. Azimi‐Sadjadi ◽  
Amanda Dinstel
Keyword(s):  
Sparse Representation ◽  
Near Field ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation

Direction‐of‐arrival estimation for far‐field acoustic signal in presence of near‐field interferences

2015 ◽  
Vol 51 (1) ◽  
pp. 101-103 ◽  
Author(s):  
Liang Zhang ◽  
Jidan Mei ◽  
Adam Zielinski ◽  
Ping Cai
Keyword(s):  
Acoustic Signal ◽  
Near Field ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Far Field

scholarly journals An Improved Two-Dimensional Direction-Of-Arrival Estimation Algorithm for L-Shaped Nested Arrays with Small Sample Sizes

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2176 ◽  
Author(s):  
Xiaofeng Gao ◽  
Xinhong Hao ◽  
Ping Li ◽  
Guolin Li
Keyword(s):  
Correlation Matrix ◽  
Degrees Of Freedom ◽  
Full Rank ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Covariance Matrices ◽  
Small Sample ◽  
Two Dimensional ◽  
Nested Arrays

In this paper, an improved two-dimensional (2-D) direction of arrival (DOA) estimation algorithm for L-shaped nested arrays is proposed. Unlike the approach for a classical nested array, which use the auto-correlation matrix (ACM) to increase the degrees of freedom (DOF), we utilize the cross-correlation matrix (CCM) of different sub-arrays to generate two long consecutive virtual arrays. These acquire a large number of DOF without redundant elements and eliminate noise effects. Furthermore, we reconstruct the CCM based on the singular value decomposition (SVD) operation in order to reduce the perturbation of noise for small numbers of samples. To cope with the matrix rank deficiency of the virtual arrays, we construct the full rank equivalent covariance matrices by using the output and its conjugate vector of virtual arrays. The unitary estimation of signal parameters via rotational invariance technique (ESPRIT) is then performed on the covariance matrices to obtain the DOA of incident signals with low computational complexity. Finally, angle pairing is achieved by deriving the equivalent signal vector of the virtual arrays using the estimated angles. Numerical simulation results show that the proposed algorithm not only provides more accurate 2-D DOA estimation performance with low complexity, but also achieves angle estimation for small numbers of samples compared to existing similar methods.

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