DOA estimation based on a modified spatial spectrum

2015 ◽  
Author(s):  
Fei Huang Fei Huang ◽  
Zhuo Zhou Zhuo Zhou ◽  
Qiaozhen Zheng Qiaozhen Zheng
Keyword(s):  
Doa Estimation ◽  
Spatial Spectrum

scholarly journals DOA Estimation for Coprime Linear Array Based on MI-ESPRIT and Lookup Table

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3043 ◽  
Author(s):  
Weike Zhang ◽  
Xi Chen ◽  
Kaibo Cui ◽  
Tao Xie ◽  
Naichang Yuan
Keyword(s):  
Linear Array ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Spatial Spectrum ◽  
Angle Measurement ◽  
Lookup Table ◽  
Signal Parameters ◽  
Estimation Precision ◽  
Simulation Results ◽  
The Relationship

In order to improve the angle measurement performance of a coprime linear array, this paper proposes a novel direction-of-arrival (DOA) estimation algorithm for a coprime linear array based on the multiple invariance estimation of signal parameters via rotational invariance techniques (MI-ESPRIT) and a lookup table method. The proposed algorithm does not require a spatial spectrum search and uses a lookup table to solve ambiguity, which reduces the computational complexity. To fully use the subarray elements, the DOA estimation precision is higher compared with existing algorithms. Moreover, the algorithm avoids the matching error when multiple signals exist by using the relationship between the signal subspace of two subarrays. Simulation results verify the effectiveness of the proposed algorithm.

scholarly journals A Sparse Representation Method for Coherent Sources Angle Estimation with Uniform Circular Array

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Xiaolong Su ◽  
Zhen Liu ◽  
Tianpeng Liu ◽  
Bo Peng ◽  
Xin Chen ◽  
...  
Keyword(s):  
Objective Function ◽  
Sparse Representation ◽  
Source Localization ◽  
Doa Estimation ◽  
Spatial Dimension ◽  
Spatial Spectrum ◽  
Circular Array ◽  
Coherent Sources ◽  
Representation Method ◽  
Coherent Source

Coherent source localization is a common problem in signal processing. In this paper, a sparse representation method is considered to deal with two-dimensional (2D) direction of arrival (DOA) estimation for coherent sources with a uniform circular array (UCA). Considering that objective function requires sparsity in the spatial dimension but does not require sparsity in time, singular value decomposition (SVD) is employed to reduce computational complexity and ℓ2 norm is utilized to renew objective function. After the new objective function is constructed to evaluate residual and sparsity, a second-order cone (SOC) programming is employed to solve convex optimization problem and obtain 2D spatial spectrum. Simulations show that the proposed method can deal with the case of coherent source localization, which has higher resolution than 2D MUSIC method and does not need to estimate the number of coherent sources in advance.

scholarly journals Underwater Ambiguity Elimination Method Based on Co-Prime Sensor Array

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6058
Author(s):  
Tian Lan ◽  
Yilin Wang ◽  
Longhao Qiu
Keyword(s):  
High Resolution ◽  
Sensor Array ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Spatial Spectrum ◽  
Estimation Methods ◽  
Sensing Applications ◽  
Passive Sensing ◽  
Spatial Spectra ◽  
Beam Patterns

Recently, the direction of arrival estimation with co-prime arrays has gradually been applied in underwater scenarios because of its significant advantages over traditional uniform linear arrays. Despite the advantages of co-prime arrays, the spatial spectra obtained directly from conventional beamforming can be degraded by grating lobes due to the sparse spatial sampling in passive sensing applications, which will seriously deteriorate the estimation performance. In this paper, capon beamforming is applied to a co-prime sensor array as a pretreatment before high-resolution direction of arrival (DOA) estimation methods. The amplitudes extracted from the beam-domain outputs of two subarrays and the phases extracted from the cross-spectrum of the spatial spectrum are exploited to suppress the spurious peaks in beam patterns and eliminate ambiguities. Consequently, interference can be further mitigated, and the performance of high-resolution DOA methods will be guaranteed. Simulations show that the method proposed can improve the reliability and accuracy of DOA estimation with great value in practice.

Improved DOA estimation via pseudorandom resampling of spatial spectrum

1997 ◽  
Vol 4 (2) ◽  
pp. 54-57 ◽  
Author(s):  
A.B. Gershman ◽  
J.F. Bohme
Keyword(s):  
Doa Estimation ◽  
Spatial Spectrum

scholarly journals DOA estimation method of spatial spectrum product for distributed coprime linear array

2021 ◽  
Vol 39 (5) ◽  
pp. 1130-1138
Author(s):  
Na WANG ◽  
Xuanzhi ZHAO ◽  
Zengli LIU ◽  
Jingjing ZHANG
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Spatial Spectrum ◽  
Estimation Accuracy ◽  
Low Snr ◽  
Distributed Array ◽  
Coprime Arrays ◽  
Coprime Array ◽  
Mathematical Derivation ◽  
Better Than

Coprime array isAsparse array composed of two uniform linear arrays with different spacing. When the two subarrays are inAnon-coherent distributed configuration, the direction of arrival (DOA) method based on the covariance analysis of the complete coprime array is no longer effective. According to the essential attribute that the distance between the elements of two subarrays can eliminate the angle ambiguity, based on the mathematical derivation, Aspatial spectral product DOA estimation method for incoherent distributed coprime arrays is proposed. Firstly, the spatial spectrum of each subarray is calculated by using the snapshot data of each subarray, and then the DOA estimation is realized by multiplying the spatial spectrum of each subarray. The simulation results show that the estimation accuracy and angle resolution of the present method are better than those of the traditional ambiguity resolution methods, and the estimation performance is good in the mutual coupling and low SNR environment, with the good adaptability and stability. Moreover, by using the flexibility of distributed array, the matching error is effectively solved through the rotation angle.

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.

Study of High Resolution DOA Estimation Based on Vector-Hydrophone

2014 ◽  
Vol 926-930 ◽  
pp. 1795-1799
Author(s):  
Hao Zhou ◽  
Zhi Jie Huo
Keyword(s):  
Doa Estimation ◽  
Spatial Spectrum ◽  
Classification Algorithm ◽  
Signal Classification ◽  
Two Dimensional ◽  
Multiple Signal Classification ◽  
Variance Matrix ◽  
Multiple Signal ◽  
Vector Hydrophone ◽  
Eigen Decomposition

Vector-hydrophone can simultaneously measure acoustic pressure and orthogonal components of the particle velocity. The 180o ambiguity in DOA estimation can be eliminated using information obtained by vector hydrophone array. Multiple signal classification algorithm is a method that takes the eigen-decomposition of data co-variance matrix to obtain the estimation of signal spatial spectrum. The two-dimensional DOA of acoustic sources is estimated based on multiple signal classification algorithm using the vector-hydrophone uniform linear array. Simulation results show that better DOA resolution performance can be obtained from vector hydrophones. Furthermore, the paper takes the de-correlation of correlated sources using spatial smoothness technology to obtain perfect performance of two-dimensional DOA estimation.

Multi-BS Spatial Spectrum Fusion for 2-D DOA Estimation and Localization Using UCA in Massive MIMO System

2021 ◽  
Vol 70 ◽  
pp. 1-13
Author(s):  
Di He ◽  
Xin Chen ◽  
Ling Pei ◽  
Fusheng Zhu ◽  
Lingge Jiang ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Mimo System ◽  
Doa Estimation ◽  
Spatial Spectrum
Sign in / Sign up

Export Citation Format

Share Document