scholarly journals DOA and Noncircular Phase Estimation of Noncircular Signal via an Improved Noncircular Rotational Invariance Propagator Method

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xueqiang Chen ◽  
Chenghua Wang ◽  
Xiaofei Zhang
Keyword(s):  
Computational Complexity ◽  
Linear Array ◽  
Rotational Invariance ◽  
Eigenvalue Decomposition ◽  
Invariance Property ◽  
Phase Estimation ◽  
Uniform Linear Array ◽  
Propagator Method ◽  
Noncircular Signal ◽  
Array Output

We consider the computationally efficient direction-of-arrival (DOA) and noncircular (NC) phase estimation problem of noncircular signal for uniform linear array. The key idea is to apply the noncircular propagator method (NC-PM) which does not require eigenvalue decomposition (EVD) of the covariance matrix or singular value decomposition (SVD) of the received data. Noncircular rotational invariance propagator method (NC-RI-PM) avoids spectral peak searching in PM and can obtain the closed-form solution of DOA, so it has lower computational complexity. Animproved NC-RI-PMalgorithm of noncircular signal for uniform linear array is proposed to estimate the elevation angles and noncircular phases with automatic pairing. We reconstruct the extended array output by combining the array output and its conjugated counterpart. Our algorithm fully uses the extended array elements in the improved propagator matrix to estimate the elevation angles and noncircular phases by utilizing the rotational invariance property between subarrays. Compared with NC-RI-PM, the proposed algorithm has better angle estimation performance and much lower computational load. The computational complexity of the proposed algorithm is analyzed. We also derive the variance of estimation error and Cramer-Rao bound (CRB) of noncircular signal for uniform linear array. Finally, simulation results are presented to demonstrate the effectiveness of our algorithm.

Two-Dimensional DOA Estimation for Planar Array Using a Successive Propagator Method

2019 ◽  
Vol 28 (10) ◽  
pp. 1950161 ◽  
Author(s):  
Weiyang Chen ◽  
Xiaofei Zhang ◽  
Chi Jiang
Keyword(s):  
Computational Cost ◽  
Doa Estimation ◽  
Rotational Invariance ◽  
Eigenvalue Decomposition ◽  
Invariance Property ◽  
Two Dimensional ◽  
Propagator Method ◽  
Planar Array ◽  
Esprit Algorithm ◽  
Better Than

We consider the problem of two-dimensional (2D) direction of arrival (DOA) estimation for planar array, and propose a successive propagator method (PM)-based algorithm. The rotational invariance property of the propagator matrix is exploited to obtain the initial angle estimations, while the accurate estimates can be achieved through successive one-dimensional and local spectrum-peak searches. The proposed algorithm can obtain automatically paired 2D-DOA estimations, and it requires no eigenvalue decomposition of the covariance matrix of received data, which remarkably reduces the computational cost compared with traditional 2D-PM algorithm. In addition, the DOA estimation performance of the proposed algorithm is better than estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm and PM algorithm, and is close to 2D-PM algorithm which requires 2D spectrum-peak search. Numerical simulations demonstrate the effectiveness and improvement of the proposed algorithm.

A Fast DOA Algorithm Based on Propagator Method and Rotation Array

2014 ◽  
Vol 614 ◽  
pp. 352-355
Author(s):  
Xiao Yu Lan ◽  
Qing Yang Guan ◽  
Yan Zou
Keyword(s):  
Computational Complexity ◽  
Time Delay ◽  
High Resolution ◽  
Fixed Time ◽  
Low Energy ◽  
Eigenvalue Decomposition ◽  
Estimation Accuracy ◽  
Base Line ◽  
Propagator Method ◽  
Accuracy Performance

It is known that computational complexity of the propagator method (PM) can be significantly smaller than those of MUSIC and ESPRIT because the PM does not require any eigenvalue decomposition (EVD) of the received data. However, most the algorithm based on PM requires more antenna elements than targets. In order to solve these issues, a novel low energy algorithm utilizing array base-line rotation for multiple targets estimation is proposed. By rotating two elements and setting a fixed time delay, even number of elements is selected to form a virtual UCA. Then, the received data of signals will be sampled at multiple positions, which improves the array elements utilization greatly. Simulations results verified the effectiveness of the proposed algorithm with high resolution and estimation accuracy performance.

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 Efficient AM Algorithms for Stochastic ML Estimation of DOA

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Haihua Chen ◽  
Shibao Li ◽  
Jianhang Liu ◽  
Yiqing Zhou ◽  
Masakiyo Suzuki
Keyword(s):  
Computational Complexity ◽  
Optimization Problem ◽  
Sensor Array ◽  
Array Signal Processing ◽  
Linear Array ◽  
Variable Parameter ◽  
Numerical Instability ◽  
Uniform Linear Array ◽  
Ml Estimation ◽  
Irreducible Form

The estimation of direction-of-arrival (DOA) of signals is a basic and important problem in sensor array signal processing. To solve this problem, many algorithms have been proposed, among which the Stochastic Maximum Likelihood (SML) is one of the most concerned algorithms because of its high accuracy of DOA. However, the estimation of SML generally involves the multidimensional nonlinear optimization problem. As a result, its computational complexity is rather high. This paper addresses the issue of reducing computational complexity of SML estimation of DOA based on the Alternating Minimization (AM) algorithm. We have the following two contributions. First using transformation of matrix and properties of spatial projection, we propose an efficient AM (EAM) algorithm by dividing the SML criterion into two components. One depends on a single variable parameter while the other does not. Second when the array is a uniform linear array, we get the irreducible form of the EAM criterion (IAM) using polynomial forms. Simulation results show that both EAM and IAM can reduce the computational complexity of SML estimation greatly, while IAM is the best. Another advantage of IAM is that this algorithm can avoid the numerical instability problem which may happen in AM and EAM algorithms when more than one parameter converges to an identical value.

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.

scholarly journals Micro Non-Uniform Linear Array (MNULA) for Ultrasound Plane Wave Imaging

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 640
Author(s):  
Yujia Tang ◽  
Zhangjian Li ◽  
Yaoyao Cui ◽  
Chen Yang ◽  
Jiabing Lv ◽  
...  
Keyword(s):  
Plane Wave ◽  
Linear Array ◽  
Near Field ◽  
Uniform Linear Array ◽  
Imaging Technology ◽  
Linear Arrays ◽  
Imaging Quality ◽  
Imaging Results ◽  
Wave Imaging ◽  
Laboratory Technology

Ultrasound plane wave imaging technology has been applied to more clinical situations than ever before because of its rapid imaging speed and stable imaging quality. Most transducers used in plane wave imaging are linear arrays, but their structures limit the application of plane wave imaging technology in some special clinical situations, especially in the endoscopic environment. In the endoscopic environment, the size of the linear array transducer is strictly miniaturized, and the imaging range is also limited to the near field. Meanwhile, the near field of a micro linear array has serious mutual interferences between elements, which is against the imaging quality of near field. Therefore, we propose a new structure of a micro ultrasound linear array for plane wave imaging. In this paper, a theoretical comparison is given through sound field and imaging simulations. On the basis of primary work and laboratory technology, micro uniform and non-uniform linear arrays were made and experimented with the phantom setting. We selected appropriate evaluation parameters to verify the imaging results. Finally, we concluded that the micro non-uniform linear array eliminated the artifacts better than the micro uniform linear array without the additional use of signal processing methods, especially for target points in the near-field. We believe this study provides a possible solution for plane wave imaging in cramped environments like endoscopy.

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