scholarly journals DOA Estimation with Spatial Spread Vector-Sensor Array Based on Biquaternion MUSIC

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qinyu Zhu ◽  
Guimei Zheng ◽  
Chen Chen ◽  
Qian Guo
Keyword(s):  
Mutual Coupling ◽  
Sensor Array ◽  
Reduction Method ◽  
Doa Estimation ◽  
Music Algorithm ◽  
Spatial Spread ◽  
Multiple Signal Classification ◽  
Vector Sensor ◽  
2D Doa Estimation ◽  
Dimensionality Reduction Method

The mutual coupling among various components of the collocated crossdipole (CCD) vector-sensor is severe, and its application is greatly limited. The spatial spread dipole (SSD) vector-sensor can avoid this problem, but the multiple signal classification (MUSIC) algorithm for the SSD array is rarely developed. In view of this situation, this paper proposed a MUSIC-like algorithm for the SSD array. The biquaternion model was first established, and the biquaternion MUSIC (BQ-MUSIC) algorithm was developed on the basis of this model, for the two-dimensional direction-of-arrival (2D-DOA) estimation. Our proposed algorithm requires low computational complexity by adopting the dimensionality reduction method. Numerical simulations verify the effectiveness of the proposed 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 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.

Two-Dimensional Direction Finding Estimation for Uniform Rectangular Array with Unknown Mutual Coupling via Real-Valued PARAFAC Decomposition

2019 ◽  
Vol 28 (03) ◽  
pp. 1950049
Author(s):  
Lingyun Xu ◽  
Fangqing Wen
Keyword(s):  
Mutual Coupling ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Music Algorithm ◽  
Rectangular Array ◽  
Lower Complexity ◽  
2D Doa Estimation ◽  
Esprit Algorithm ◽  
Parafac Decomposition ◽  
Uniform Rectangular Array

Two-dimensional direction-of-arrival (2D-DOA) estimation for uniform rectangular array (URA) is a canonical problem with numerous applications, e.g., wireless communications, sonar and radar systems. The conventional 2D-DOA estimators usually are derived with the assumption of ideal arrays. However, in practice, the arrays may not be well calibrated and suffer from unknown mutual coupling. Using the conventional estimators may lead to low accuracy estimation and high computational complexity in the condition of large number of array elements. In this paper, a novel real-valued parallel factor (PARAFAC) decomposition algorithm is proposed to tackle this problem. The proposed algorithm has better angle estimation performance than the multiple signal classification (MUSIC) algorithm, estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm and conventional PARAFAC algorithm. But it has lower complexity than MUSIC algorithm. Moreover, the proposed algorithm can obtain automatically paired 2D-DOA estimation, and it is suitable to coherent or closely spaced signals and can eliminate the mutual coupling. Simulation results verify the effectiveness of the proposed algorithm.

Quaternion min-norm algorithm for DOA estimation with acoustic vector sensor array

2014 ◽  
Vol 22 (7) ◽  
pp. 1969-1975
Author(s):  
李新波 LI Xin-bo ◽  
李晓青 LI Xiao-qing ◽  
刘国君 LIU Guo-jun ◽  
石要武 SHI Yao-wu ◽  
杨志刚 YANG Zhi-gang
Keyword(s):  
Sensor Array ◽  
Doa Estimation ◽  
Acoustic Vector Sensor ◽  
Vector Sensor ◽  
Vector Sensor Array

scholarly journals Polarization and DOA Estimation Based on Dual-Polarized Conformal Array

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Chao Liu ◽  
Shuai Xiang ◽  
Liangfeng Xu ◽  
Zhengfei Fang
Keyword(s):  
Doa Estimation ◽  
Signal Classification ◽  
Orthogonal Polarization ◽  
Coherent Signals ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Polarization Signal ◽  
Conformal Array ◽  
Dual Polarized ◽  
Cramer Rao Bound

A dual-polarized multiple signal classification (DP-MUSIC) algorithm is presented to estimate the arrival directions and polarizations for a dual-polarized conformal array. Each polarization signal is decomposed into two orthogonal polarization components, which are considered to be a pair of coherent signals coming from the same direction but different polarization. The polarization parameters are modeled as the equivalent coherence coefficients of the orthogonal polarization components. Then, the method of decoherence can be used to decouple the information of polarization states and signal angles. After that, the direction of arrival (DOA) and polarization parameters can be estimated by the DP-MUSIC algorithm. Moreover, the angles of incident direction are re-estimated, which greatly improves the accuracy of DOA estimation. The Cramer–Rao bound (CRB) is derived and the effectiveness of the proposed algorithm is verified by Monte Carlo simulations.

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