Weighted spatial bispectrum correlation matrix for DOA estimation in the presence of interferences

2014 ◽  
Author(s):  
Wei Xue ◽  
Shan Liang ◽  
Wenju Liu
Keyword(s):  
Correlation Matrix ◽  
Doa Estimation

USTC-MUSIC Algorithm for DOA Estimation of Coherent Sources Using Single Vector Hydrophone

2014 ◽  
Vol 530-531 ◽  
pp. 530-533
Author(s):  
Jin Fang Cheng ◽  
Chao Ran Zhang ◽  
Wei Zhang
Keyword(s):  
Correlation Matrix ◽  
Temporal Correlation ◽  
Doa Estimation ◽  
Spatial Smoothing ◽  
Music Algorithm ◽  
Correlation Matrices ◽  
Vector Hydrophone ◽  
Coherent Sources ◽  
Simulation Results ◽  
Spatio Temporal

The MUSIC algorithm cannot deal with the problem of DOA estimation of coherent sources, this paper proposes the USTC (unitary spatio-temporal correlation matrices)-MUSIC algorithm using single vector hydrophone to solve this problem, by utilizing the unitary spatio-temporal correlation matrix instead of the covariance matrix. The simulation results demonstrate that the USTC-MUSIC algorithm has a better ability to distinguish the coherent sources from different directions than the spatial smoothing MUSIC algorithm.

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.

scholarly journals DOA Estimation for Multiple Targets in MIMO Radar with Nonorthogonal Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Zhenxin Cao ◽  
Peng Chen ◽  
Zhimin Chen ◽  
Yi Jin
Keyword(s):  
Correlation Matrix ◽  
Cross Correlation ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Signal Classification ◽  
Multiple Signal Classification ◽  
Estimation Performance ◽  
Multiple Input ◽  
Input Multiple Output

This paper addresses the direction of arrival (DOA) estimation problem in the colocated multiple-input multiple-output (MIMO) radar with nonorthogonal signals. The maximum number of targets that can be estimated is theoretically derived as rankRsN, where N denotes the number of receiving antennas and Rs is the cross-correlation matrix of the transmitted signals. Therefore, with the rank-deficient cross-correlation matrix, the maximum number that can be estimated is less than the radar with orthogonal signals. Then, a multiple signal classification- (MUSIC-) based algorithm is given for the nonorthogonal signals. Furthermore, the DOA estimation performance is also theoretically analyzed by the Carmér-Rao lower bound. Simulation results show that the nonorthogonality degrades the DOA estimation performance only in the scenario with the rank-deficient cross-correlation matrix.

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