Wideband DOA estimation with deficient snapshots using low rank Toeplitz structure

2019 ◽  
Vol 55 (17) ◽  
pp. 961-963 ◽  
Author(s):  
Juan Shi ◽  
Qunfei Zhang ◽  
Wen Tao Shi
Keyword(s):  
Doa Estimation ◽  
Low Rank ◽  
Toeplitz Structure

DOA Estimation for Antenna Array with Partially-Well Sensors via Low-Rank Matrix Completion

2013 ◽  
Vol 756-759 ◽  
pp. 3977-3981 ◽  
Author(s):  
Hua Xing Yu ◽  
Xiao Fei Zhang ◽  
Jian Feng Li ◽  
De Ben
Keyword(s):  
Missing Values ◽  
Linear Array ◽  
Matrix Completion ◽  
Doa Estimation ◽  
Low Rank ◽  
Uniform Linear Array ◽  
Completion Problem ◽  
Rank Matrix ◽  
Low Rank Matrix Completion ◽  
Low Rank Matrix

In this paper, we address the angle estimation problem in linear array with some ill sensors (partially-well sensors), which only work well randomly. The output of the array will miss some values, and this can be regarded as a low-rank matrix completion problem due to the property that the number of sources is smaller than the number of the total sensors. The output of the array, which is corrupted by the missing values and the noise, can be complete via the Optspace method, and then the angles can be estimated according to the complete output. The proposed algorithm works well for the array with some ill sensors; moreover, it is suitable for non-uniform linear array. Simulation results illustrate performance of the algorithm.

scholarly journals Covariance Matrix Reconstruction for Direction Finding with Nested Arrays Using Iterative Reweighted Nuclear Norm Minimization

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Weijie Tan ◽  
Xi’an Feng
Keyword(s):  
Covariance Matrix ◽  
Regularization Parameter ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Direction Finding ◽  
Nuclear Norm ◽  
Low Rank ◽  
Matrix Reconstruction ◽  
Linear Transform

In this paper, we address the direction finding problem in the background of unknown nonuniform noise with nested array. A novel gridless direction finding method is proposed via the low-rank covariance matrix approximation, which is based on a reweighted nuclear norm optimization. In the proposed method, we first eliminate the noise variance variable by linear transform and utilize the covariance fitting criteria to determine the regularization parameter for insuring robustness. And then we reconstruct the low-rank covariance matrix by iteratively reweighted nuclear norm optimization that imposes the nonconvex penalty. Finally, we exploit the search-free DoA estimation method to perform the parameter estimation. Numerical simulations are carried out to verify the effectiveness of the proposed method. Moreover, results indicate that the proposed method has more accurate DoA estimation in the nonuniform noise and off-grid cases compared with the state-of-the-art DoA estimation algorithm.

scholarly journals Root-MUSIC Based Angle Estimation for MIMO Radar with Unknown Mutual Coupling

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jianfeng Li ◽  
Xiaofei Zhang ◽  
Weiyang Chen
Keyword(s):  
Mutual Coupling ◽  
Estimation Error ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Lower Complexity ◽  
Toeplitz Structure

Direction of arrival (DOA) estimation problem for multiple-input multiple-output (MIMO) radar with unknown mutual coupling is studied, and an algorithm for the DOA estimation based on root multiple signal classification (MUSIC) is proposed. Firstly, according to the Toeplitz structure of the mutual coupling matrix, output data of some specified sensors are selected to eliminate the influence of the mutual coupling. Then the reduced-dimension transformation is applied to make the computation burden lower as well as obtain a Vandermonde structure of the direction matrix. Finally, Root-MUSIC can be adopted for the angle estimation. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm and MUSIC-like algorithm. Furthermore, the proposed algorithm has lower complexity than them. The simulation results verify the effectiveness of the algorithm, and the theoretical estimation error of the algorithm is also derived.

scholarly journals An APG-MUSIC Algorithm Based-on Optimized Sampling Array

2018 ◽  
Vol 208 ◽  
pp. 01004
Author(s):  
Mengxia Li ◽  
Wen Hu ◽  
Jiaying Di ◽  
Hongtao Li
Keyword(s):  
Null Space ◽  
Matrix Completion ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Sparse Sampling ◽  
Spatial Spectrum ◽  
Low Rank ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
2D Doa Estimation

This paper proposes a novel two-dimensional direction of arrival (2D-DOA) estimation with optimized sparse sampling array, which is combined with Accelerated Proximal Gradient singular value thresholding(APG) and Multiple Signal Classification(MUSIC). Firstly, a signal model of 2D-DOA estimation in sparse array is established, which is proved to satisfy low rank feature and NULL Space Property(NSP). Then, Genetic algorithm (GA) is applied to a sparse sampling array to optimize the performance of matrix completion(MC). Finally, MUSIC combined with APG is studied to recover received signal matrix and estimate the direction of arrival. The results of computer simulation demonstrate that compared with conventional 2D-DOA algorithms, the proposed algorithm reduces the number of array elements needed dramatically and effectively lowers the average sidelobes level of spatial spectrum.

scholarly journals A Priori-Based Subarray Selection Algorithm for DOA Estimation

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4626
Author(s):  
Linghao Zeng ◽  
Guanghua Zhang ◽  
Chongzhao Han
Keyword(s):  
Coupling Effect ◽  
A Priori ◽  
Doa Estimation ◽  
Low Rank ◽  
Sensing Matrix ◽  
New Approach ◽  
Rank Matrix ◽  
Original Array ◽  
Low Rank Matrix ◽  
High Possibility

A finer direction-of-arrival (DOA) estimation result needs a large and dense array; it may, however, encounter the mutual coupling effect, which degrades the performance of DOA estimation. There is a new approach to mitigating this effect by using a nonuniform array to achieve DOA estimation. In this paper, we consider a priori DOA estimation, which is easily obtained from tracking results. The a priori DOA requires us to pay close attention to the high possibility of where the DOA will appear; then, a weight according to the prior probability distribution of DOA is added to each direction, which leads the sensing matrix of DOA estimation to be near low-rank. Thus, according to the low-rank matrix approximation theory, an optimal low-rank approximate matrix is obtained and an algorithm is proposed to select the elements of the original array according to right singular vectors of the approximate matrix. After that, the impacts of different weights are analyzed, and a mixed weight is presented which has flexibility for common use. Finally, a number of numerical simulations are carried out, and the results verify the effectiveness of the proposed methods.

scholarly journals Gridless DOA Estimation for Minimum-Redundancy Linear Array in Nonuniform Noise

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Changyun Qi ◽  
Gong Zhang ◽  
Jiawen Yuan
Keyword(s):  
Covariance Matrix ◽  
Linear Array ◽  
Estimation Method ◽  
Doa Estimation ◽  
Residual Energy ◽  
Estimation Accuracy ◽  
Two Stages ◽  
Signal Covariance Matrix ◽  
Toeplitz Structure ◽  
True Signal

A gridless direction-of-arrival (DOA) estimation method to improve the estimation accuracy and resolution in nonuniform noise is proposed in this paper. This algorithm adopts the structure of minimum-redundancy linear array (MRA) and can be composed of two stages. In the first stage, by minimizing the rank of the covariance matrix of the true signal, the covariance matrix that filters out nonuniform noise is obtained, and then a gridless residual energy constraint scheme is designed to reconstruct the signal covariance matrix of the Hermitian Toeplitz structure. Finally, the unknown DOAs can be determined from the recovered covariance matrix, and the number of sources can be acquired as a byproduct. The proposed algorithm can be regarded as a gridless version method based on sparsity. Simulation results indicate that the proposed method has higher estimation accuracy and resolution compared with existing algorithms.

Sign in / Sign up

Export Citation Format

Share Document