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.

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 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.

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.

scholarly journals Fast DOA Estimation Algorithms for Sparse Uniform Linear Array With Multiple Integer Frequencies

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 29952-29965 ◽  
Author(s):  
Aihua Liu ◽  
Xin Zhang ◽  
Qiang Yang ◽  
Weibo Deng
Keyword(s):  
Linear Array ◽  
Doa Estimation ◽  
Uniform Linear Array ◽  
Estimation Algorithms

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 of Noncircular Signals for Coprime Linear Array via Polynomial Root-Finding Technique

2020 ◽  
pp. 2150028
Author(s):  
Hui Zhai ◽  
Zheng Li ◽  
Xiaofei Zhang
Keyword(s):  
Linear Array ◽  
Doa Estimation ◽  
Music Algorithm ◽  
Uniform Linear Array ◽  
Estimation Algorithms ◽  
Root Finding ◽  
Noncircular Signals ◽  
Peak Search ◽  
Simulation Results ◽  
Polynomial Root Finding

In this paper, we investigate the direction of arrival (DOA) estimation problem of noncircular signals for coprime linear array (CLA). From the perspective of the CLA as extracted from a filled uniform linear array (ULA), a noncircular root-MUSIC algorithm is proposed to estimate the DOA which can avoid the spectral peak search and lower the computational complexity. Due to the noncircular characteristic, the proposed algorithm enables to resolve more sources than sensors. Meanwhile, the proposed algorithm has better angle estimation performance than some conventional DOA estimation algorithms. Numerical simulation results illustrate the performance of the proposed method.

scholarly journals Wideband DOA Estimation on Co-prime Array via Atomic Norm Minimization

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3235 ◽  
Author(s):  
Hyeonjin Chung ◽  
Young Mi Park ◽  
Sunwoo Kim
Keyword(s):  
Compressive Sensing ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Low Complexity ◽  
Time Saving ◽  
Estimation Algorithms ◽  
Norm Minimization ◽  
Atomic Norm ◽  
Multiple Measurement Vector ◽  
Low Dimensional

This paper introduces a low complexity wideband direction-of-arrival (DOA) estimation algorithm on the co-prime array. To increase the number of the detectable signal sources and to prevent an unnecessary increase in complexity, the low dimensional co-prime array vector is constructed by arranging elements of the correlation matrix at every frequency bin. The atomic norm minimization (ANM)-based approach resolves the grid-mismatch, which causes an inevitable error in the compressive sensing (CS)-based DOA estimation. However, the complexity surges when the ANM is exploited to the wideband DOA estimation on the co-prime array. The surging complexity of the ANM-based wideband DOA estimation on the co-prime array is handled by solving the time-saving semidefinite programming (SDP) motivated by the ANM for multiple measurement vector (MMV) case. Simulation results show that the proposed algorithm has high accuracy and low complexity compared to compressive sensing (CS)-based wideband DOA estimation algorithms that exploit the co-prime array.

scholarly journals Compressive Sensing for High-Resolution Direction-of-Arrival Estimation via Iterative Optimization on Sensing Matrix

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Hongtao Li ◽  
Chaoyu Wang ◽  
Xiaohua Zhu
Keyword(s):  
Compressive Sensing ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Sensing Matrix ◽  
Dantzig Selector ◽  
Iterative Optimization ◽  
Estimation Algorithms ◽  
Sensing Model

A novel compressive sensing- (CS-) based direction-of-arrival (DOA) estimation algorithm is proposed to solve the performance degradation of the CS-based DOA estimation in the presence of sensing matrix mismatching. Firstly, a DOA sparse sensing model is set up in the presence of sensing matrix mismatching. Secondly, combining the Dantzig selector (DS) algorithm and least-absolute shrinkage and selection operator (LASSO) algorithm, a CS-based DOA estimation algorithm which performs iterative optimization alternatively on target angle information vector and sensing matrix mismatching error vector is proposed. The simulation result indicates that the proposed algorithm possesses higher angle resolution and estimation accuracy compared with conventional CS-based DOA estimation algorithms.

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