scholarly journals 2D-DOA Estimation for EMVS Array with Nonuniform Noise

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chang-Xin Cai ◽  
Guan-Jun Huang ◽  
Fang-Qing Wen ◽  
Xin-Hai Wang ◽  
Lin Wang
Keyword(s):  
Matrix Completion ◽  
Doa Estimation ◽  
Cross Product ◽  
Completion Problem ◽  
Vector Sensor ◽  
Electromagnetic Vector Sensor ◽  
Product Technique ◽  
Source Signal ◽  
2D Doa Estimation ◽  
Sensor Geometry

Electromagnetic vector sensor (EMVS) array is one of the most potential arrays for future wireless communications and radars because it is capable of providing two-dimensional (2D) direction-of-arrival (DOA) estimation as well as polarization angles of the source signal. It is well known that existing subspace algorithm cannot directly be applied to a nonuniform noise scenario. In this paper, we consider the 2D-DOA estimation issue for EMVS array in the presence of nonuniform noise and propose an improved subspace-based algorithm. Firstly, it recasts the nonuniform noise issue as a matrix completion problem. The noiseless array covariance matrix is then recovered via solving a convex optimization problem. Thereafter, the shift invariant principle of the EMVS array is adopted to construct a normalized polarization steering vector, after which 2D-DOA is easily estimated as well as polarization angles by incorporating the vector cross-product technique and the pseudoinverse method. The proposed algorithm is effective to EMVS array with arbitrary sensor geometry. Furthermore, the proposed algorithm is free from the nonuniform noise. Several simulations verify the improvement of the proposed method.

scholarly journals Source Localization Using Distributed Electromagnetic Vector Sensors

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Tingping Zhang ◽  
Di Wan ◽  
Xinhai Wang ◽  
Fangqing Wen
Keyword(s):  
Least Squares Method ◽  
Doa Estimation ◽  
Cross Product ◽  
Tensor Model ◽  
Arbitrary Geometry ◽  
Vector Sensor ◽  
Array Architecture ◽  
Product Technique ◽  
Array Measurement ◽  
Value Decomposition

Electromagnetic vector sensor (EVS) array has drawn extensive attention in the past decades, since it offers two-dimensional direction-of-arrival (2D-DOA) estimation and additional polarization information of the incoming source. Most of the existing works concerning EVS array are focused on parameter estimation with special array architecture, e.g., uniform manifold and sparse arrays. In this paper, we consider a more general scenario that EVS array is distributed in an arbitrary geometry, and a novel estimator is proposed. Firstly, the covariance tensor model is established, which can make full use of the multidimensional structure of the array measurement. Then, the higher-order singular value decomposition (HOSVD) is adopted to obtain a more accurate signal subspace. Thereafter, a novel rotation invariant relation is exploited to construct a normalized Poynting vector, and the vector cross-product technique is utilized to estimate the 2D-DOA. Based on the previous obtained 2D-DOA, the polarization parameter can be easily achieved via the least squares method. The proposed method is suitable for EVS array with arbitrary geometry, and it is insensitive to the spatially colored noise. Therefore, it is more flexible than the state-of-the-art algorithms. Finally, numerical simulations are carried out to verify the effectiveness of the proposed estimator.

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.

2D DOA Estimation for Coherent Signals with Acoustic Vector-Sensor Array

2016 ◽  
Vol 95 (2) ◽  
pp. 1285-1297 ◽  
Author(s):  
Sheng Liu ◽  
Lisheng Yang ◽  
Yuanju Xie ◽  
Yanhong Yin ◽  
Qingping Jiang
Keyword(s):  
Sensor Array ◽  
Doa Estimation ◽  
Coherent Signals ◽  
Acoustic Vector Sensor ◽  
Vector Sensor ◽  
2D Doa Estimation ◽  
Vector Sensor Array

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.

Underwater Electromagnetic Sources 2-D DOA and Polarization Estimation

2012 ◽  
Vol 459 ◽  
pp. 529-534
Author(s):  
Zhen Bin Zhang ◽  
Bin Li
Keyword(s):  
Poynting Vector ◽  
Cross Product ◽  
Parameter Estimates ◽  
Data Sets ◽  
Rotation Invariant ◽  
Vector Sensor ◽  
Specific Distribution ◽  
Electromagnetic Vector Sensor ◽  
Polarization Estimation ◽  
Esprit Algorithm

With the development of ocean exploration and research, the precise estimation of underwater electromagnetic sources’ DOA and polarization has become an essential research topic. An electromagnetic vector sensor can measure all six electromagnetic field components of the incident wave field. Thus, it explores not only the DOA of the signal but also the polarization diversity which represents electromagnetic sources’ specific distribution. TLS-ESPRIT algorithm is based on rotation invariant of two signal subspaces. It utilizes a pair of temporally displace data sets collected form a single electromagnetic vector sensor. The parameter estimates are obtained through Poynting vector that is cross-product operation of each signal subspace eigenvector of the data correlation matrix. This method requires no priori knowledge of signal frequencies, suffers no frequency ambiguity, can resolve up to five uncorrelated monochromatic underwater sources. Simulations illustrated the efficiency of the algorithm.

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 Two-Dimensional DOA Estimation for Monostatic MIMO Radar with Electromagnetic Vector Received Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Guimei Zheng ◽  
Jun Tang
Keyword(s):  
Electromagnetic Field ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Field Vector ◽  
Cross Product ◽  
Mimo Radar ◽  
Two Dimensional ◽  
Array Configuration ◽  
2D Doa Estimation

We study two-dimensional direction of arrival (2D-DOA) estimation problem of monostatic MIMO radar with the receiving array which consists of electromagnetic vector sensors (EMVSs). The proposed angle estimation algorithm can be applied to the arbitrary and unknown array configuration, which can be summarized as follows. Firstly, EMVSs in the receiver of a monostatic MIMO radar are used to measure all six electromagnetic-field components of an incident wavefield. The vector sensor array with the six unknown electromagnetic-field components is divided into six spatially identical subarrays. Secondly, ESPRIT is utilized to estimate the rotational invariant factors (RIFs). Parts of the RIFs are picked up to restore the source’s electromagnetic-field vector. Last, a vector cross product operation is performed between electric field and magnetic field to obtain the Pointing vector, which can offer the 2D-DOA estimation. Prior knowledge of array elements’ positions and angle searching procedure are not necessary for the proposed 2D-DOA estimation method. Simulation results prove the validity of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document