scholarly journals Sparse DOA Estimation Algorithm Based on Fourth-Order Cumulants Vector Exploiting Restricted Non-Uniform Linear Array

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 9980-9988 ◽  
Author(s):  
Tao Chen ◽  
Lin Shi ◽  
Limin Guo
Keyword(s):  
Fourth Order ◽  
Linear Array ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Uniform Linear Array

Under the Uniform Linear Array Related Research of Broadband Signal DOA Estimation Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 2698-2701
Author(s):  
Fa Tang Chen ◽  
Lu Gang Wang ◽  
Dan Wang
Keyword(s):  
Frequency Band ◽  
Linear Array ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Signal Frequency ◽  
Music Algorithm ◽  
Uniform Linear Array ◽  
Narrowband Signal ◽  
Broadband Signal ◽  
Signal Doa Estimation

In order to solve the positioning problem of radar and communications, broadband signal and improve the positioning accuracy, this paper proposes a method of DOA estimation under uniform linear array, the method based on fast Fourier transform, using narrowband MUSIC algorithm principle, the broadband signal decomposition for multiple narrowband signal frequency band, and then for each frequency band for narrowband signal DOA estimation. The simulation results show that the algorithm has good performance.

DOA Estimation Algorithm for Non-Uniform Linear Array with Parallel Factor and Power Loading

2014 ◽  
Vol 556-562 ◽  
pp. 3361-3364
Author(s):  
Chi Jiang ◽  
Xiao Fei Zhang ◽  
Li Cen Zhang
Keyword(s):  
Linear Array ◽  
Simulation Analysis ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Signal Source ◽  
Signal To Noise ◽  
Uniform Linear Array ◽  
Power Loading ◽  
Parallel Factor

The algorithm of DOA estimation for non-uniform linear array with Parallel Factor (PARAFAC) and power loading is carried out detailed studies and simulation in this paper, and we use Trilinear Alternating Least Squares (TALS) estimation algorithm to estimate the DOA of signal source. In addition, we make a simulation analysis and comparison of different parameters (Signal-to-noise ratio, the number of snapshots, the number of antenna elements, the number of targets, the similar angles) and different algorithm. Finally the thesis summarizes the work.

scholarly journals Off-Grid DoA Estimation on Non-Uniform Linear Array Using Constrained Hermitian Matrix

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5775
Author(s):  
Hyeonjin Chung ◽  
Jeungmin Joo ◽  
Sunwoo Kim
Keyword(s):  
Toeplitz Matrix ◽  
Linear Array ◽  
Hermitian Matrix ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Uniform Linear Array ◽  
Atomic Norm ◽  
Array Structures ◽  
Simulation Results

In this paper, an off-grid direction-of-arrival (DoA) estimation algorithm which can work on a non-uniform linear array (NULA) is proposed. The original semidefinite programming (SDP) representation of the atomic norm exploits a summation of one-rank matrices constructed by atoms, where the summation of one-rank matrices equals a Hermitian Toeplitz matrix when using the uniform linear array (ULA). On the other hand, when the antennas in the array are placed arbitrarily, the summation of one-rank matrices is a Hermitian matrix whose diagonal elements are equivalent. Motivated by this property, the proposed algorithm replaces the Hermitian Toeplitz matrix in the original SDP with the constrained Hermitian matrix. Additionally, when the antennas are placed symmetrically, the performance can be enforced by adding extra constraints to the Hermitian matrix. The simulation results show that the proposed algorithm achieves higher estimation accuracy and resolution than other algorithms on both array structures; i.e., the arbitrary array and the symmetric array.

DOA Estimation for Uniform Linear Array with Compressive Sensing

2015 ◽  
Vol 713-715 ◽  
pp. 1239-1243
Author(s):  
Ying Zhang ◽  
Guang Yao Xin ◽  
Xiao Fei Zhang
Keyword(s):  
Compressive Sensing ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
High Sensitivity ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Signal To Noise ◽  
Uniform Linear Array ◽  
High Demand ◽  
Estimation Algorithms

This paper discusses that the application of compressive sensing in direction of arrival (DOA) estimation. Traditional DOA estimation algorithms, such as MUSIC, ESPRIT, have shortcomings of high demand of initialization and sufficient number of snapshots and high sensitivity to signal-to-noise ratio (SNR). The proposed DOA estimation algorithm via OMP method based on compressed sensing (CS) can solve the above-mentioned problem and has good estimation performance. Computer simulations verify the effectiveness of the OMP algorithm.

scholarly journals A Low Complexity Subspace-Based DOA Estimation Algorithm with Uniform Linear Array Correlation Matrix Subsampling

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Do-Sik Yoo
Keyword(s):  
Linear Array ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Low Complexity ◽  
Eigenvalue Decomposition ◽  
Complex Eigenvalue ◽  
Uniform Linear Array ◽  
Autocorrelation Matrix ◽  
Value Decomposition

We propose a low complexity subspace-based direction-of-arrival (DOA) estimation algorithm employing a direct signal space construction method (DSPCM) by subsampling the autocorrelation matrix of a uniform linear array (ULA). Three major contributions of this paper are as follows. First of all, we introduce the method of autocorrelation matrix subsampling which enables us to employ a low complexity algorithm based on a ULA without computationally complex eigenvalue decomposition or singular-value decomposition. Secondly, we introduce a signal vector separation method to improve the distinguishability among signal vectors, which can greatly improve the performance, particularly, in low signal-to-noise ratio (SNR) regime. Thirdly, we provide a root finding (RF) method in addition to a spectral search (SS) method as the angle finding scheme. Through simulations, we illustrate that the performance of the proposed scheme is reasonably close to computationally much more expensive MUSIC- (MUltiple SIgnal Classification-) based algorithms. Finally, we illustrate that the computational complexity of the proposed scheme is reduced, in comparison with those of MUSIC-based schemes, by a factor ofO(N2/K), whereKis the number of sources andNis the number of antenna elements.

scholarly journals A fourth-order cumulant orthonormal propagator rooting method based on Toeplitz approximation

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Heping Shi ◽  
Ning Ma ◽  
Zhiwei Guan ◽  
Lizhu Zhang ◽  
Shan Jiang
Keyword(s):  
Computational Complexity ◽  
Fourth Order ◽  
Linear Array ◽  
Doa Estimation ◽  
Uniform Linear Array ◽  
Color Noise ◽  
Noise Environment ◽  
Reduced Dimension ◽  
Fourth Order Cumulant ◽  
Toeplitz Structure

Abstract A novel Toeplitz fourth-order cumulant (FOC) orthonormal propagator rooting method (TFOC ‐ OPRM) of direction-of-arrival (DOA) estimation for uniform linear array (ULA) is proposed in this paper. Specifically, the modified (i.e., reduced-dimension) FOC  (MFOC) matrix is achieved at first via removing the redundant information encompassed in the primary FOC matrix; then, the TFOC matrix which possesses Toeplitz structure can be recovered by utilizing the Toeplitz approximation method. To reduce the computational complexity, an effective method based on the polynomial rooting technology is adopted. Finally, the DOAs of incident signals can be estimated by exploiting orthonormal propagator rooting method. The theoretical analysis coupled with simulation results show that the proposed resultant algorithm can reduce the computational complexity significantly, as well as improve the estimation performance in both spatially white noise environment and spatially color noise environment.

scholarly journals A Fourth-Order Cumulants Orthonormal Propagator Rooting Method Based on Toeplitz Approximation

2020 ◽  
Author(s):  
Heping Shi ◽  
Ning Ma ◽  
Zhiwei Guan ◽  
Lizhu Zhang ◽  
Shan Jiang
Keyword(s):  
Computational Complexity ◽  
Theoretical Analysis ◽  
Fourth Order ◽  
Linear Array ◽  
Doa Estimation ◽  
Redundant Information ◽  
Uniform Linear Array ◽  
Color Noise ◽  
Reduced Dimension ◽  
Simulation Results

Abstract A novel Toeplitz fourth-order cumulants (\operatorname{FOC} ) orthonormal propagator rooting method {\text{(TFOC-OPRM)}} to direction-of-arrival (DOA) estimation for uniform linear array (ULA) is addressed in this paper. Specifically, the modified (reduced-dimension) FOC{\kern 1pt} {\kern 1pt} (MFOC) matrix is achieved at first via removing the redundant information encompassed in the primary FOC matrix, and then the TFOC matrix which possesses Toepltiz structure can be recovered by utilizing the Toepltiz approximation method. To reduce computational complexity, we adopt an effective method which depends on the polynomial rooting technology. Finally, the DOAs of incident signals can be estimated by exploiting orthonormal propagator rooting method. The theoretical analysis coupled with simulation results show that the proposed resultant algorithm can reduce computational complexity significantly, as well as improve the estimation performance in both spatially-white noise and spatially-color noise environments.

Comparison between DOA Estimation Subspace methods for incoherent and coherent signals

2015 ◽  
Vol 1 ◽  
pp. 18
Author(s):  
Ahmed Abdalla ◽  
Suhad Mohammed ◽  
Tang Bin ◽  
Jumma Mary Atieno ◽  
Abdelazeim Abdalla
Keyword(s):  
Linear Array ◽  
Doa Estimation ◽  
Far Field ◽  
Matlab Simulation ◽  
Subspace Methods ◽  
Coherent Signals ◽  
Uniform Linear Array ◽  
Pros And Cons ◽  
Basic Concepts ◽  
And Performance

This paper considers the problem of estimating the direction of arrival (DOA) for the both incoherent and coherent signals from narrowband sources, located in the far field in the case of uniform linear array sensors. Three different methods are analyzed. Specifically, these methods are Music, Root-Music and ESPRIT. The pros and cons of these methods are identified and compared in light of different viewpoints. The performance of the three methods is evaluated, analytically, when possible, and by Matlab simulation. This paper can be a roadmap for beginners in understanding the basic concepts of DOA estimation issues, properties and performance.

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