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.

scholarly journals White noise reduction for wideband linear array signal processing

2018 ◽  
Vol 12 (3) ◽  
pp. 335-345 ◽  
Author(s):  
Mohammad Reza Anbiyaei ◽  
Wei Liu ◽  
Des C. McLernon
Keyword(s):  
Signal Processing ◽  
White Noise ◽  
Noise Reduction ◽  
Array Signal Processing ◽  
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.

Method to Estimate and Promote SNR of Covariance Matrix in Array Signal Processing

2013 ◽  
Vol 397-400 ◽  
pp. 2156-2160
Author(s):  
Yi Ran Shi ◽  
Yan Tao Tian ◽  
Hong Wei Shi ◽  
Lan Xiang Zhu
Keyword(s):  
Signal Processing ◽  
Covariance Matrix ◽  
Array Signal Processing ◽  
Doa Estimation ◽  
Accurate Estimation ◽  
Music Algorithm ◽  
Important Work ◽  
Variance Matrix ◽  
Eigen Value ◽  
The Difference

Estimation for direction of arrival (DOA) is an important work in array signal processing, and subspace method such as MUSIC algorithm is basic and important in DOA estimation. This paper analyzes the structure of eigen value of variance matrix, and proposes a method to estimate the signal noise ratio (SNR) of the data received by sensor array. With the accurate estimation for SNR, we can estimate the work environment and decide detect threshold for many algorithm. The paper also proposes a method to promote the SNR of covariance matrix with moving the covariance slice to do DOA estimation. It can efficiently widen the difference of signal eigen value and noise eigen value.

scholarly journals Software-Assisted Depth Analysis of Optic Nerve Stereoscopic Images in Telemedicine

2016 ◽  
Vol 2016 ◽  
pp. 1-5
Author(s):  
Tian Xia ◽  
Shriji N. Patel ◽  
Ben C. Szirth ◽  
Anton M. Kolomeyer ◽  
Albert S. Khouri
Keyword(s):  
Optic Nerve ◽  
Intraclass Correlation ◽  
Normal Subjects ◽  
Stereo Pair ◽  
Depth Analysis ◽  
The Difference ◽  
The Right ◽  
A Prospective Study ◽  
High Degree ◽  
Cup To Disc Ratio

Background. Software guided optic nerve assessment can assist in process automation and reduce interobserver disagreement. We tested depth analysis software (DAS) in assessing optic nerve cup-to-disc ratio (VCD) from stereoscopic optic nerve images (SONI) of normal eyes.Methods. In a prospective study, simultaneous SONI from normal subjects were collected during telemedicine screenings using a Kowa 3Wx nonmydriatic simultaneous stereoscopic retinal camera (Tokyo, Japan). VCD was determined from SONI pairs and proprietary pixel DAS (Kowa Inc., Tokyo, Japan) after disc and cup contour line placement. A nonstereoscopic VCD was determined using the right channel of a stereo pair. Mean, standard deviation,t-test, and the intraclass correlation coefficient (ICCC) were calculated.Results. 32 patients had mean age of40±14years. Mean VCD on SONI was0.36±0.09, with DAS0.38±0.08, and with nonstereoscopic0.29±0.12. The difference between stereoscopic and DAS assisted was not significant (p=0.45). ICCC showed agreement between stereoscopic and software VCD assessment. Mean VCD difference was significant between nonstereoscopic and stereoscopic (p<0.05) and nonstereoscopic and DAS (p<0.005) recordings.Conclusions. DAS successfully assessed SONI and showed a high degree of correlation to physician-determined stereoscopic VCD.

scholarly journals Direction-of-Arrival Estimation in Coprime Array Using the ESPRIT-Based Method

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 707 ◽  
Author(s):  
Zhen Meng ◽  
Weidong Zhou
Keyword(s):  
Covariance Matrix ◽  
Toeplitz Matrix ◽  
Linear Array ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Reconstruction Method ◽  
Uniform Linear Array ◽  
Matrix Reconstruction ◽  
Coprime Array ◽  
Observation Vector

Coprime arrays have shown potential advantages for direction-of-arrival (DOA) estimation by increasing the number of degrees-of-freedom in the difference coarray domain with fewer physical sensors. In this paper, a new DOA estimation algorithm for coprime array based on the estimation of signal parameter via rotational invariance techniques (ESPRIT) is proposed. We firstly derive the observation vector of the virtual uniform linear array but the covariance matrix of this observation vector is rank-deficient. Different from the traditional Toeplitz matrix reconstruction method using the observation vector, we propose a modified Toeplitz matrix reconstruction method using any non-zero row of the covariance matrix in the virtual uniform linear array. It can be proved in theory that the reconstructed Toeplitz covariance matrix has full rank. Therefore, the improved ESPRIT method can be used for DOA estimation without peak searching. Finally, the closed-form solution for DOA estimation in coprime array is obtained. Compared to the traditional coprime multiple signal classification (MUSIC) methods, the proposed method circumvents the use of spatial smoothing technique, which usually results in performance degradation and heavy computational burden. The effectiveness of the proposed method is demonstrated by numerical examples.

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.

Legal Update: North Dakota Supreme Court: Third Edition of Guides is “Most Current”

1999 ◽  
Vol 4 (1) ◽  
pp. 6-7
Author(s):  
James J. Mangraviti
Keyword(s):  
Internal Rotation ◽  
External Rotation ◽  
Measurement Techniques ◽  
Fourth Edition ◽  
Hip Motion ◽  
The Difference ◽  
The Right ◽  
Hip Flexion ◽  
Hip Rotation ◽  
Hip Extension

Abstract The accurate measurement of hip motion is critical when one rates impairments of this joint, makes an initial diagnosis, assesses progression over time, and evaluates treatment outcome. The hip permits all motions typical of a ball-and-socket joint. The hip sacrifices some motion but gains stability and strength. Figures 52 to 54 in AMA Guides to the Evaluation of Permanent Impairment (AMA Guides), Fourth Edition, illustrate techniques for measuring hip flexion, loss of extension, abduction, adduction, and external and internal rotation. Figure 53 in the AMA Guides, Fourth Edition, illustrates neutral, abducted, and adducted positions of the hip and proper alignment of the goniometer arms, and Figure 52 illustrates use of a goniometer to measure flexion of the right hip. In terms of impairment rating, hip extension (at least any beyond neutral) is irrelevant, and the AMA Guides contains no figures describing its measurement. Figure 54, Measuring Internal and External Hip Rotation, demonstrates proper positioning and measurement techniques for rotary movements of this joint. The difference between measured and actual hip rotation probably is minimal and is irrelevant for impairment rating. The normal internal rotation varies from 30° to 40°, and the external rotation ranges from 40° to 60°.

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