Subspace extension algorithm for 2D DOA estimation with L-shaped sparse array

2016 ◽  
Vol 28 (1) ◽  
pp. 315-327 ◽  
Author(s):  
Sheng Liu ◽  
Lisheng Yang ◽  
Dong Li ◽  
Hailin Cao
Keyword(s):  
Doa Estimation ◽  
Sparse Array ◽  
2D Doa Estimation

scholarly journals A FPC-ROOT Algorithm for 2D-DOA Estimation in Sparse Array

2016 ◽  
Vol 2016 ◽  
pp. 1-6
Author(s):  
Wenhao Zeng ◽  
Hongtao Li ◽  
Xiaohua Zhu ◽  
Chaoyu Wang
Keyword(s):  
Null Space ◽  
Matrix Completion ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Polynomial Roots ◽  
Sparse Array ◽  
Autocorrelation Matrix ◽  
Proposed Model ◽  
The Matrix ◽  
2D Doa Estimation

To improve the performance of two-dimensional direction-of-arrival (2D DOA) estimation in sparse array, this paper presents a Fixed Point Continuation Polynomial Roots (FPC-ROOT) algorithm. Firstly, a signal model for DOA estimation is established based on matrix completion and it can be proved that the proposed model meets Null Space Property (NSP). Secondly, left and right singular vectors of received signals matrix are achieved using the matrix completion algorithm. Finally, 2D DOA estimation can be acquired through solving the polynomial roots. The proposed algorithm can achieve high accuracy of 2D DOA estimation in sparse array, without solving autocorrelation matrix of received signals and scanning of two-dimensional spectral peak. Besides, it decreases the number of antennas and lowers computational complexity and meanwhile avoids the angle ambiguity problem. Computer simulations demonstrate that the proposed FPC-ROOT algorithm can obtain the 2D DOA estimation precisely in sparse array.

2D DOA estimation for noncircular sources using L-shaped sparse array

2016 ◽  
Vol 29 (2) ◽  
pp. 489-502 ◽  
Author(s):  
Sheng Liu ◽  
Lisheng Yang ◽  
Dong Li ◽  
Hailin Cao ◽  
Qingping Jiang
Keyword(s):  
Doa Estimation ◽  
Sparse Array ◽  
2D Doa Estimation

scholarly journals Improved subspace‐based method for 2D DOA estimation with L‐shaped array

2020 ◽  
Vol 56 (8) ◽  
pp. 402-405
Author(s):  
Mengyi Liu ◽  
Hui Cao ◽  
Yuntao Wu
Keyword(s):  
Doa Estimation ◽  
2D Doa Estimation

2D DOA Estimation Using Sparse Representation of Higher-Order Power of Covariance Matrix

2014 ◽  
Vol 998-999 ◽  
pp. 779-783
Author(s):  
Zheng Luo ◽  
Fei Yu ◽  
Lin Wu ◽  
Yuan Liu
Keyword(s):  
Covariance Matrix ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Higher Order ◽  
Eigenvalue Decomposition ◽  
Coherent Signals ◽  
Sparse Decomposition ◽  
Sparse Signal Representation ◽  
2D Doa Estimation ◽  
Source Signals

A novel two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm utilizing a sparse signal representation of higher-order power of covariance matrix is proposed. Through applying the higher-order power of covariance matrix to construct a new sparse decomposition vector, this algorithm avoids the estimation of incident signal number and eigenvalue decomposition. And the hierarchical granularity-dictionary is studied, which forms the over-complete dictionary adaptively in the light of source signals’ distribution. Compared with MUSIC and L1-SVD, this algorithm not only provides a better 2D DOA performance but also possesses the capability of coherent signals estimation. Theoretical analysis and simulation results demonstrate the validity and robust of the proposed algorithm.

Sign in / Sign up

Export Citation Format

Share Document