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.

Nested sensor array extension factors required to match the peak sidelobe height of a uniform linear array

2019 ◽  
Vol 145 (3) ◽  
pp. 1733-1733
Author(s):  
Hossam Elsaadawy ◽  
Kayla M. Houte ◽  
Camille LeBlanc ◽  
James M. Slezak ◽  
Kaushallya Adhikari
Keyword(s):  
Sensor Array ◽  
Linear Array ◽  
Uniform Linear Array ◽  
Array Extension

scholarly journals A Novel Nested Configuration Based on the Difference and Sum Co-Array Concept

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2988 ◽  
Author(s):  
Zhenhong Chen ◽  
Yingtao Ding ◽  
Shiwei Ren ◽  
Zhiming Chen
Keyword(s):  
Signal Processing ◽  
Array Signal Processing ◽  
Linear Array ◽  
Difference Set ◽  
Uniform Linear Array ◽  
The Difference ◽  
The Right ◽  
Coprime Array ◽  
High Degree ◽  
Sensor Locations

Recently, the concept of the difference and sum co-array (DSCa) has attracted much attention in array signal processing due to its high degree of freedom (DOF). In this paper, the DSCa of the nested array (NA) is analyzed and then an improved nested configuration known as the diff-sum nested array (DsNA) is proposed. We find and prove that the sum set for the NA contains all the elements in the difference set. Thus, there exists the dual characteristic between the two sets, i.e., for the difference result between any two sensor locations of the NA, one equivalent non-negative/non-positive sum result of two other sensor locations can always be found. In order to reduce the redundancy for further DOF enhancement, we develop a new DsNA configuration by moving nearly half the dense sensors of the NA to the right side of the sparse uniform linear array (ULA) part. These moved sensors together with the original sparse ULA form an extended sparse ULA. For analysis, we provide the closed form expressions of the DsNA locations as well as the DOF. Compared with some novel sparse arrays with large aperture such as the NA, coprime array and augmented nested array, the DsNA can achieve a higher number of DOF. The effectiveness of the proposed array is proved by the simulations.

On the Numerical Instability of an LCMV Beamformer for a Uniform Linear Array

2016 ◽  
Vol 23 (2) ◽  
pp. 272-276 ◽  
Author(s):  
Soumitro Chakrabarty ◽  
Emanuel A. P. Habets
Keyword(s):  
Linear Array ◽  
Numerical Instability ◽  
Uniform Linear Array

scholarly journals SVD-based angular difference estimation

2020 ◽  
Author(s):  
Dazhuan Xu ◽  
Ying Zhou ◽  
Weilin Tu ◽  
Chao Shi
Keyword(s):  
Gaussian Noise ◽  
Array Signal Processing ◽  
Linear Array ◽  
Estimation Method ◽  
Singular Value ◽  
Spatial Covariance ◽  
Angular Difference ◽  
Uniform Linear Array ◽  
Actual Environment ◽  
Value Decomposition

Abstract In this paper, the estimation of angular difference between adjacent sources is investigated in the uniform linear array. In the presence of additive Gaussian noise, we use singular value decomposition to obtain the eigenvalues of the spatial covariance matrix. Thus, the expressions of eigenvalues related to the signal are obtained. The smaller one between the eigenvalues is related to the degree of difference between the two sources and we derive the expression of the angular difference estimation through it. In practice, the more samples observed, the more accurate the estimate is. Numerical results confirm our theoretical analysis and demonstrate the effectiveness of the proposed estimation method. The result in this paper has important guiding significance for array signal processing in the actual environment.

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.

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.

Sensor Array Self-Calibration for Two-Dimensional Direction of Arrival Estimation

2012 ◽  
Vol 490-495 ◽  
pp. 534-537
Author(s):  
Da Wei Xiao ◽  
Jin Fang Cheng ◽  
Yi Liu
Keyword(s):  
Sensor Array ◽  
Linear Array ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Calibration Method ◽  
Estimation Accuracy ◽  
Two Dimensional ◽  
Uniform Linear Array ◽  
Actual Measurement ◽  
Self Calibration

In recent years, high-resolution Direction of Arrival (DOA) estimation with a sensor array has become indispensable for various applications. In actual measurement, however, DOA estimation accuracy is deteriorated by many error factors. For a uniform linear array (ULA), there exist algorithms for self-calibration for single-dimensional (1-D) DOA estimation. In this paper, we develop a simple self-calibration method for two-dimensional (2-D) DOA estimation with an L-shaped array.

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.

A hybrid neural network goal attain optimization for failed sensor(s) radiation pattern in linear array

2019 ◽  
Vol 4 (4) ◽  
pp. 101-109
Author(s):  
Navaamsini Boopalan ◽  
Agileswari K. Ramasamy ◽  
Farrukh Hafiz Nagi
Keyword(s):  
Neural Network ◽  
Radiation Pattern ◽  
Array Signal Processing ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
Beam Pattern ◽  
Sensor Failure ◽  
Signal To Noise ◽  
Hybrid Neural Network ◽  
Noise Ratio

Array sensors are widely used in various fields such as radar, wireless communications, autonomous vehicle applications, medical imaging, and astronomical observations fault diagnosis. Array signal processing is accomplished with a beam pattern which is produced by the signal's amplitude and phase at each element of array. The beam pattern can get rigorously distorted in case of failure of array element and effect its Signal to Noise Ratio (SNR) badly. This paper proposes on a Hybrid Neural Network layer weight Goal Attain Optimization (HNNGAO) method to generate a recovery beam pattern which closely resembles the original beam pattern with remaining elements in the array. The proposed HNNGAO method is compared with classic synthesize beam pattern goal attain method and failed beam pattern generated in MATLAB environment. The results obtained proves that the proposed HNNGAO method gives better SNR ratio with remaining working element in linear array compared to classic goal attain method alone. Keywords: Backpropagation; Feed-forward neural network; Goal attain; Neural networks; Radiation pattern; Sensor arrays; Sensor failure; Signal-to-Noise Ratio (SNR)

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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