Improved 2-D DOA Estimation Algorithm for L-shaped Array Using Cross-correlation Matrix

2017 ◽  
Author(s):  
Hui Zhang ◽  
Hao Chen
Keyword(s):  
Correlation Matrix ◽  
Cross Correlation ◽  
Estimation Algorithm ◽  
Doa Estimation

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.

Central DOA Estimation of Incoherently Distributed Noncircular Sources with Cross-Correlation Matrix

2015 ◽  
Vol 34 (11) ◽  
pp. 3697-3707 ◽  
Author(s):  
Xuemin Yang ◽  
Guangjun Li ◽  
Chi Chung Ko ◽  
Zhi Zheng ◽  
Tat Soon Yeo
Keyword(s):  
Correlation Matrix ◽  
Cross Correlation ◽  
Doa Estimation ◽  
Central Doa Estimation

scholarly journals DOA Estimation Based on Real-Valued Cross Correlation Matrix of Coprime Arrays

Sensors ◽  
2017 ◽  
Vol 17 (3) ◽  
pp. 638 ◽  
Author(s):  
Jianfeng Li ◽  
Feng Wang ◽  
Defu Jiang
Keyword(s):  
Correlation Matrix ◽  
Cross Correlation ◽  
Doa Estimation ◽  
Coprime Arrays

scholarly journals An Efficient Framework for Estimating the Direction of Multiple Sound Sources Using Higher-Order Generalized Singular Value Decomposition

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2977 ◽  
Author(s):  
Bandhit Suksiri ◽  
Masahiro Fukumoto
Keyword(s):  
Singular Value Decomposition ◽  
Correlation Matrix ◽  
Cross Correlation ◽  
Estimation Algorithm ◽  
Higher Order ◽  
Singular Value ◽  
Generalized Singular Value Decomposition ◽  
Sound Sources ◽  
Correlation Matrices ◽  
Value Decomposition

This paper presents an efficient framework for estimating the direction-of-arrival (DOA) of wideband sound sources. The proposed framework provides an efficient way to construct a wideband cross-correlation matrix from multiple narrowband cross-correlation matrices for all frequency bins. In addition, the proposed framework is inspired by the coherent signal subspace technique with further improvement of linear transformation procedure, and the new procedure no longer requires any process of DOA preliminary estimation by exploiting unique cross-correlation matrices between the received signal and itself on distinct frequencies, along with the higher-order generalized singular value decomposition of the array of this unique matrix. Wideband DOAs are estimated by employing any subspace-based technique for estimating narrowband DOAs, but using the proposed wideband correlation instead of the narrowband correlation matrix. It implies that the proposed framework enables cutting-edge studies in the recent narrowband subspace methods to estimate DOAs of the wideband sources directly, which result in reducing computational complexity and facilitating the estimation algorithm. Practical examples are presented to showcase its applicability and effectiveness, and the results show that the performance of fusion methods perform better than others over a range of signal-to-noise ratios with just a few sensors, which make it suitable for practical use.

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