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Journal articles on the topic 'Matrix array'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Maringa, Maina, and LM Masu. "The effects of different fibre packing geometries on the transverse matrix strain magnification and fibre strain reduction in uniaxially aligned continuous fibre-reinforced composites." Journal of Composite Materials 50, no. 29 (July 28, 2016): 4159–70. http://dx.doi.org/10.1177/0021998316631701.

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Expressions for transverse matrix strain magnification and fibre strain reduction are derived for square and hexagonal fibre array reinforced composites. Respective transverse matrix and fibre strain magnification and reduction, for the square arrays are shown to be higher for all reinforcing fibre volume fractions than those for the hexagonal arrays. The respective magnification and reduction of the transverse matrix and fibre strains are shown to decrease with increasing values of the ratio of elastic modulus ( Em/ Ef) for both reinforcing fibre arrays. The magnified transverse matrix strains in axially loaded longitudinally aligned continuous fibre-reinforced composites are shown to be higher than the applied longitudinal strains for all square array reinforcing fibre volume fractions and for all hexagonal array reinforcing fibre volumes fractions above 31%. This raises possibilities of longitudinal matrix splitting before interfacial bond failure and transverse matrix failure, in a strain based rather than stress-based failure mode.

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Xu, Le, Rui Li, Xiaoqun Chen, Feng Wei, and Xiaowei Shi. "Wideband Frequency Invariant Array Synthesis Based on Matrix Singular Value Decomposition." Electronics 10, no. 16 (August 23, 2021): 2039. http://dx.doi.org/10.3390/electronics10162039.

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In this paper, an analytic method for frequency invariant (FI) array synthesis is proposed based on matrix singular value decomposition. By grouping the elements of FI array into a few subarrays, the FI pattern in the whole frequency band is realized. Using this algorithm, the number of sub arrays is reduced. Simulation results show that the proposed algorithm can synthesize the 64-element broadband FI array in 0.52 s. For the 18-element linear array, the half power beam width (HPBW) changes less than 0.6 degrees in the bandwidth. Moreover, the range of HPBW variation decreases rapidly along with the increase in the number of elements. Furthermore, the effectiveness of the algorithm is verified by synthesizing FI array with low side lobe level (SLL), beam scanning, and notch requirements. The examples in this paper show that the proposed algorithm can achieve better pattern characteristics with fewer elements. Finally, a broadband antenna with 2:1 bandwidth is improved, and two FI arrays of 23 elements and 64 elements are formed by using the antenna. The active pattern of the array element is introduced into the proposed algorithm, and two FI arrays synthesized by the algorithm are simulated by full wave software.

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Matsushita, Yohsuke, Hiroyuki Shimada, Takuya Miyashita, Miki Shibata, Shigeki Naka, Hiroyuki Okada, and Hiroyoshi Onnagawa. "Organic Bi-function Matrix Array." Japanese Journal of Applied Physics 44, no. 4B (April 21, 2005): 2826–29. http://dx.doi.org/10.1143/jjap.44.2826.

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Pelli, P., H. Elfström, K. Jefimovs, J. Aikio, M. Karppinen, P. Vahimaa, and M. Kuittinen. "Replicated data-matrix array generators." Optics Communications 260, no. 1 (April 2006): 329–36. http://dx.doi.org/10.1016/j.optcom.2005.10.016.

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Kim, K., and S. R. Saunders. "Adaptive antenna array using real symmetric array covariance matrix." Electronics Letters 37, no. 7 (2001): 405. http://dx.doi.org/10.1049/el:20010281.

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6

Zheng, Mei-yan, Ke-song Chen, Hong-gang Wu, and Xian-pan Liu. "Sparse Planar Array Synthesis Using Matrix Enhancement and Matrix Pencil." International Journal of Antennas and Propagation 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/147097.

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The matrix enhancement and matrix pencil (MEMP) plays important roles in modern signal processing applications. In this paper, MEMP is applied to attack the problem of two-dimensional sparse array synthesis. Firstly, the desired array radiation pattern, as the original pattern for approximating, is sampled to form an enhanced matrix. After performing the singular value decomposition (SVD) and discarding the insignificant singular values according to the prior approximate error, the minimum number of elements can be obtained. Secondly, in order to obtain the eigenvalues, the generalized eigen-decomposition is employed on the approximate matrix, which is the optimal low-rank approximation of the enhanced matrix corresponding to sparse planar array, and then the ESPRIT algorithm is utilized to pair the eigenvalues related to each dimension of the planar array. Finally, element positions and excitations of the sparse planar array are calculated according to the correct pairing of eigenvalues. Simulation results are presented to illustrate the effectiveness of the proposed approach.

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SUBRAMANIAN, K. G., KALPANA MAHALINGAM, ROSNI ABDULLAH, and ATULYA K. NAGAR. "TWO-DIMENSIONAL DIGITIZED PICTURE ARRAYS AND PARIKH MATRICES." International Journal of Foundations of Computer Science 24, no. 03 (April 2013): 393–408. http://dx.doi.org/10.1142/s012905411350010x.

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Parikh matrix mapping or Parikh matrix of a word has been introduced in the literature to count the scattered subwords in the word. Several properties of a Parikh matrix have been extensively investigated. A picture array is a two-dimensional connected digitized rectangular array consisting of a finite number of pixels with each pixel in a cell having a label from a finite alphabet. Here we extend the notion of Parikh matrix of a word to a picture array and associate with it two kinds of Parikh matrices, called row Parikh matrix and column Parikh matrix. Two picture arrays A and B are defined to be M-equivalent if their row Parikh matrices are the same and their column Parikh matrices are the same. This enables to extend the notion of M-ambiguity to a picture array. In the binary and ternary cases, conditions that ensure M-ambiguity are then obtained.

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Wu, Tao, Yiwen Li, Zhengxin Li, Yijie Huang, and Jiwei Xu. "A 2D Nested Array Based DOA Estimator for Incoherently Distributed Sources via Sparse Representation Utilizing L1-Norm." International Journal of Antennas and Propagation 2019 (July 3, 2019): 1–11. http://dx.doi.org/10.1155/2019/6941963.

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Nested arrays are sparse arrays composed of subarrays with nonuniform sensor spacing. Compared with traditional uniform arrays, nested arrays have more degree of freedoms (DOFs) and larger apertures. In this paper, a nested array has been proposed as well as a direction-of-arrival (DOA) estimation method for two-dimensional (2D) incoherently distributed (ID) sources. A virtual array is firstly obtained through vectorization of the cross-correlation matrix of subarrays. Sensor positions of the virtual array and the optimal configuration of the nested array are derived next. Then rotational invariance relationship for generalized steering matrix of the virtual array with respect to nominal azimuth is deduced. According to the rotational invariance relationship, sparse representation model under l1-norm constraint is established, which is resolved by transferring the objective function to second-order cone constraints and combining a estimation residual error constraint for receive vector of the virtual array. Simulations are conducted to investigate the effectiveness of the proposed method in underdetermined situation and examine different experiment factors including SNR, snapshots, and angular spreads as well as sensor number of subarrays. Results show that the proposed method has better performance than uniform parallel arrays with the same number of sensors.

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Di, Jiaying, Wen Hu, Mengxia Li, and Hongtao Li. "An optimized 2D-Robust Adaptive Beamforming algorithm based on Matrix Completion in sparse array." MATEC Web of Conferences 208 (2018): 01003. http://dx.doi.org/10.1051/matecconf/201820801003.

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The sparse arrays can reduce the cost of beamforming, it greatly reduces the number of actual array elements. However, it also brings about the problem of information loss. A 2D-robust adaptive beamforming algorithm in sparse array based on Singular Value Thresholding algorithm is proposed. At first, a signal model of planar array is established based on Matrix Completion, which can be proved to meet Null Space Property. Then the Genetic Algorithm is used to optimize the sparse array, which is determined to reduce the Spectral Norm Error of Matrix Completion and make the array recovered closer to the full array. In the case of sparse array, the missing information is restored by using the theory of Singular Value Thresholding, and then the restored signal is used to design the digital beamformer weights. This algorithm significantly reduces the Spectral Norm Error and forms robust adaptive beam.

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10

Jiang, Wei, Guodong Qin, and Jian Dong. "DOA Estimation for a Passive Synthetic Array Based on Cross-Correlation Matrix." International Journal of Signal Processing Systems 5, no. 2 (June 2017): 55–59. http://dx.doi.org/10.18178/ijsps.5.2.55-59.

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Sadovskyy, Ivan A. "Reduction of the scattering matrix array." Uspekhi Fizicheskih Nauk 185, no. 9 (2015): 941–45. http://dx.doi.org/10.3367/ufnr.0185.201509c.0941.

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Sadovskyy, I. A. "Reduction of the scattering matrix array." Physics-Uspekhi 58, no. 9 (September 30, 2015): 872–76. http://dx.doi.org/10.3367/ufne.0185.201509c.0941.

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13

Ray, S., R. M. Kolbas, M. J. Hafich, and B. E. Dies. "Monolithic matrix-addressable AlGaAs—GaAs array." IEEE Transactions on Electron Devices 33, no. 6 (June 1986): 845–49. http://dx.doi.org/10.1109/t-ed.1986.22577.

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Ciminiera, L., A. Serra, and A. Valenzano. "Partitioned array for stable matrix triangularisation." IEE Proceedings E Computers and Digital Techniques 133, no. 1 (1986): 45. http://dx.doi.org/10.1049/ip-e.1986.0004.

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El-Amawy, A., W. A. Porter, and J. L. Aravena. "Array architectures for iterative matrix calculations." IEE Proceedings E Computers and Digital Techniques 134, no. 3 (1987): 149. http://dx.doi.org/10.1049/ip-e.1987.0027.

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Mei, Fengtong, Daming Wang, Chunxiao Jian, Yinsheng Wang, and Weijia Cui. "A Design Method of Sparse Array with High Degrees of Freedom Based on Fourth-Order Cumulants." Mathematical Problems in Engineering 2021 (July 1, 2021): 1–13. http://dx.doi.org/10.1155/2021/9915963.

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Recently, the design of sparse linear array for direction of arrival (DOA) estimation of non-Gaussian signals has attracted considerable interest due to the fact that the fourth-order difference coarray offered by non-Gaussian significantly increases the aperture of a virtual linear array, which improves the performance of DOA estimation. In this paper, a super four-level nested array (S-FL-NA) configuration based on fourth-order cumulants (FOC) is proposed. The S-FL-NA consists of uniform linear arrays which have different interelement spacing. The proposed array configuration is designed based on interelement spacing, which, for a given number of sensors, is uniquely determined by a closed-form expression. We also derive the closed-form expression for the degrees of freedom (DOFs) of the proposed array. The optimal distribution of the number of sensors in each uniform linear array of the proposed array is given for an arbitrary number of sensors. Compared with the existing sparse arrays, the proposed array can provide a higher number of degrees of freedom and a larger physical array aperture. In addition, to improve the calculation speed of the fourth-order cumulant matrix, we simplify the FOC matrix by removing some redundancy. Numerical simulations are conducted to verify the superiority of the S-FL-NA over other sparse arrays.

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17

Friedlander, Benjamin. "Antenna Array Calibration for a General Linear Transformation of the Array Manifold." International Journal of Antennas and Propagation 2019 (April 9, 2019): 1–11. http://dx.doi.org/10.1155/2019/9257853.

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We consider the problem of calibrating an antenna array using received signals from either known or unknown directions. A complex matrix is used to map the assumed array manifold to the true array manifold. This matrix may be completely unstructured. Algorithms are presented for joint estimation of this matrix and the directions-of-arrival, using data collected during multiple time intervals.

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18

Tokarsky, Peter L. "Mutual Impedance Properties in a Lossy Array Antenna." International Journal of Antennas and Propagation 2019 (November 11, 2019): 1–9. http://dx.doi.org/10.1155/2019/1687497.

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The impedance matrix of an arbitrary multiport array antenna with Ohmic losses was studied. It was assumed that the partial current distributions in the array, corresponding to the alternate exciting one of its terminals while the other ones are open-circuited, are known. Consideration of the power balance in the lossy array antenna has allowed ascertaining that its impedance matrix is a sum of the radiation resistance matrix and loss resistance matrix, which are in general case complex Hermitian matrices and only in some particular cases can be real. The theoretical statements obtained are confirmed by two numerical examples, where analysis of two lossy dipole arrays was performed. In the first example, the dipoles were located above the imperfect ground, which served as a source of losses, and in the second example, the same dipoles were located in free space, and the embedded parasitic element was the source of losses. The results of the analysis showed that the asymmetric placement of energy absorbers in the array antennas leads to the appearance of imaginary parts in the matrices of radiation and loss resistances, which allow one to correctly predict the behavior of the array radiation efficiency during beam scanning.

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Sun, Bing, Chenxi Wu, and Huailin Ruan. "Array Diagnosis and DOA Estimation for Coprime Array under Sensor Failures." Sensors 20, no. 9 (May 11, 2020): 2735. http://dx.doi.org/10.3390/s20092735.

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A coprime array of N sensors can achieve O ( N 2 ) degrees of freedom (DOFs) by possessing a uniform linear array segment of size O ( N 2 ) in the difference coarray. However, the structure of difference coarray is sensitive to sensor failures. Once the sensor fails, the impact of failure sensors on the coarray structure may decrease the DOFs and cause direction finding failure. Therefore, the direction of arrival (DOA) estimation of coprime arrays with sensor failures is a significant but challenging topic for investigation. Driven by the need for remedial measures, an efficient detection strategy is developed to diagnose the coprime array. Furthermore, based on the difference coarray, we divide the sensor failures into two scenarios. For redundant sensor failure scenarios, the structure of difference coarray remains unchanged, and the coarray MUSIC (CO-MUSIC) algorithm is applied for DOA estimation. For non-redundant sensor failure scenarios, the consecutive lags of the difference coarray will contain holes, which hinder the application of CO-MUSIC. We employ Singular Value Thresholding (SVT) algorithm to fill the holes with covariance matrix reconstruction. Specifically, the covariance matrix is reconstructed into a matrix with zero elements, and the SVT algorithm is employed to perform matrix completion, thereby filling the holes. Finally, we employ root-MUSIC for DOA estimation. Simulation results verify the effectiveness of the proposed methods.

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Risset, Tanguy. "Implementing Gaussian elimination on a matrix-matrix multiplication systolic array." Parallel Computing 16, no. 2-3 (December 1990): 351–59. http://dx.doi.org/10.1016/0167-8191(90)90072-h.

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Meng, Zhen, and Weidong Zhou. "Direction-of-Arrival Estimation in Coprime Array Using the ESPRIT-Based Method." Sensors 19, no. 3 (February 9, 2019): 707. http://dx.doi.org/10.3390/s19030707.

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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.

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Hosseini, Seyed MohammadReza, and Mohammad Ali Sebt. "Array Interpolation Using Covariance Matrix Completion of Minimum-Size Virtual Array." IEEE Signal Processing Letters 24, no. 7 (July 2017): 1063–67. http://dx.doi.org/10.1109/lsp.2017.2708750.

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Klymchuk, Tetiana. "Regularizing algorithm for mixed matrix pencils." Applied Mathematics and Nonlinear Sciences 2, no. 1 (April 18, 2017): 123–30. http://dx.doi.org/10.21042/amns.2017.1.00010.

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AbstractP. Van Dooren (1979) constructed an algorithm for computing all singular summands of Kronecker’s canonical form of a matrix pencil. His algorithm uses only unitary transformations, which improves its numerical stability. We extend Van Dooren’s algorithm to square complex matrices with respect to consimilarity transformations $\begin{array}{} \displaystyle A \mapsto SA{\bar S^{ - 1}} \end{array}$ and to pairs of m × n complex matrices with respect to transformations $\begin{array}{} \displaystyle (A,B) \mapsto (SAR,SB\bar R) \end{array}$, in which S and R are nonsingular matrices.

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Li, Wenxing, Xiaojun Mao, Wenhua Yu, and Chongyi Yue. "An Effective Technique for Enhancing Direction Finding Performance of Virtual Arrays." International Journal of Antennas and Propagation 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/728463.

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The array interpolation technology that is used to establish a virtual array from a real antenna array is widely used in direction finding. The traditional interpolation transformation technology causes significant bias in the directional-of-arrival (DOA) estimation due to its transform errors. In this paper, we proposed a modified interpolation method that significantly reduces bias in the DOA estimation of a virtual antenna array and improves the resolution capability. Using the projection concept, this paper projects the transformation matrix into the real array data covariance matrix; the operation not only enhances the signal subspace but also improves the orthogonality between the signal and noise subspace. Numerical results demonstrate the effectiveness of the proposed method. The proposed method can achieve better DOA estimation accuracy of virtual arrays and has a high resolution performance compared to the traditional interpolation method.

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Ihedrane, Mohammed Amine, Seddik Bri, and El Fadl Adiba. "High Resolution Method using Patch Circular Array." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (August 1, 2017): 2116. http://dx.doi.org/10.11591/ijece.v7i4.pp2116-2124.

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Smart antennas have recently received increasing for improving the performance of wireless radio systems. In this research article, we have used a patch antenna using uniform circular arrays (UCA) with central element for direction of arrival (DOA). A central element was added to arrays in order to increase steering capability of the proposed array. This geometry is used to determine the elevation and azimuth based on two famous algorithms of high resolution method: Matrix Pencil method (MP) and MUltiple Signal Classification (MUSIC).The comparison results demonstrate clearly that the matrix pencil is more accurate and stable to estimation of direction of arrival compared to the MUSIC algorithm.

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TUĞLU, Naim, Fatma YEŞİL, Maciej DZIEMIAŃCZUK, and E. Gökçen KOÇER. "$q$-Riordan array for $q$-Pascal matrix and its inverse matrix." TURKISH JOURNAL OF MATHEMATICS 40 (2016): 1038–48. http://dx.doi.org/10.3906/mat-1506-56.

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Yang, Boquan, Shengguo Shi, and Desen Yang. "Acoustic source localization using the open spherical microphone array in the low-frequency range." MATEC Web of Conferences 283 (2019): 04001. http://dx.doi.org/10.1051/matecconf/201928304001.

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Recently, spherical microphone arrays (SMA) have become increasingly significant for source localization and identification in three dimension due to its spherical symmetry. However, conventional Spherical Harmonic Beamforming (SHB) based on SMA has limitations, such as poor resolution and high side-lobe levels in image maps. To overcome these limitations, this paper employs the iterative generalized inverse beamforming algorithm with a virtual extrapolated open spherical microphone array. The sidelobes can be suppressed and the main-lobe can be narrowed by introducing the two iteration processes into the generalized inverse beamforming (GIB) algorithm. The instability caused by uncertainties in actual measurements, such as measurement noise and configuration problems in the process of GIB, can be minimized by iteratively redefining the form of regularization matrix and the corresponding GIB localization results. In addition, the poor performance of microphone arrays in the low-frequency range due to the array aperture can be improved by using a virtual extrapolated open spherical array (EA), which has a larger array aperture. The virtual array is obtained by a kind of data preprocessing method through the regularization matrix algorithm. Both results from simulations and experiments show the feasibility and accuracy of the method.

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Taddei, Fabrizio, Laura Franceschetti, Giuliano Farina, Federico Prefumo, Marino Signorelli, Nicola Fratelli, and Caterina Groli. "Matrix Array Transducers in Fetal Heart Imaging." Donald School Journal of Ultrasound in Obstetrics and Gynecology 1, no. 3 (2007): 45–48. http://dx.doi.org/10.5005/jp-journals-10009-1107.

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Evans, D. J. "Systolic array designs for matrix iterative processes." International Journal of Computer Mathematics 73, no. 4 (January 2000): 405–15. http://dx.doi.org/10.1080/00207160008804906.

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Chuang, Henry Y. H., and Guo He. "A versatile systolic array for matrix computations." ACM SIGARCH Computer Architecture News 13, no. 3 (June 1985): 315–22. http://dx.doi.org/10.1145/327070.327285.

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Chatterjee, S., A. R. Lebeck, P. K. Patnala, and M. Thottethodi. "Recursive array layouts and fast matrix multiplication." IEEE Transactions on Parallel and Distributed Systems 13, no. 11 (November 2002): 1105–23. http://dx.doi.org/10.1109/tpds.2002.1058095.

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Du, W., and R. L. Kirlin. "Enhancement of covariance matrix for array processing." IEEE Transactions on Signal Processing 40, no. 10 (1992): 2602–6. http://dx.doi.org/10.1109/78.157303.

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Zhang, Yang, Junyi Zhai, and Zhong Lin Wang. "Piezo-Phototronic Matrix via a Nanowire Array." Small 13, no. 46 (October 23, 2017): 1702377. http://dx.doi.org/10.1002/smll.201702377.

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Gusev, M., and D. J. Evans. "A New Matrix Vector Product Systolic Array." Journal of Parallel and Distributed Computing 22, no. 2 (August 1994): 346–49. http://dx.doi.org/10.1006/jpdc.1994.1094.

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Milovanović, E. I., I. Ž Milovanović, M. K. Stojčev, and G. S. Jovanović. "Fault-tolerant matrix inversion on processor array." Electronics Letters 28, no. 13 (1992): 1206. http://dx.doi.org/10.1049/el:19920762.

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Findeklee, Christian. "Array Noise Matching via the Scattering Matrix." IEEE Transactions on Antennas and Propagation 67, no. 4 (April 2019): 2344–53. http://dx.doi.org/10.1109/tap.2019.2893229.

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Milovanović, I. Ž., E. I. Milovanović, and M. K. Stojčev. "Matrix inversion algorithm for linear array processor." Mathematical and Computer Modelling 16, no. 12 (December 1992): 133–41. http://dx.doi.org/10.1016/0895-7177(92)90026-h.

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Moreno, Jaime H., Miguel E. Figueroa, and Tomas Lang. "Linear pseudosystolic array for partitioned matrix algorithms." Journal of VLSI signal processing systems for signal, image and video technology 3, no. 3 (September 1991): 201–14. http://dx.doi.org/10.1007/bf00925831.

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Sun, Hong Yan, Chun Yang Wang, Yan Xin Yu, Yu Chen, and Xue Mei Wang. "A New Method of Doppler Frequency and DOA Estimation Based on Gradient-Time-Delay." Applied Mechanics and Materials 716-717 (December 2014): 1298–302. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.1298.

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This paper proposes that arrays of spatio-temporal data matrix was constructed by the arrays of the received signal delayed in time gradient, Meanwhile, applying the method of ESPIRT to get the twiddle factor of the received signal array element and the array element of the time and the space, finally get the final information of Doppler frequency and the information of arrival angle though applying least squares method.

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Snopce, Halil, and Azir Aliu. "Latency Analysis in the 2-Dimensional Systolic Arrays for Matrix Multiplication." International Journal of Computers 15 (March 22, 2021): 1–7. http://dx.doi.org/10.46300/9108.2021.15.1.

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This paper deals with the latency analysis in a twodimensional systolic array for matrix multiplication. The latency for all possible connection schemes is discussed. In this way there is obtained the lower bound of the latency that can be achieved using such arrays.

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Liu, Zhang-Meng, Zhi-Tao Huang, and Yi-Yu Zhou. "Array Signal Processing via Sparsity-Inducing Representation of the Array Covariance Matrix." IEEE Transactions on Aerospace and Electronic Systems 49, no. 3 (July 2013): 1710–24. http://dx.doi.org/10.1109/taes.2013.6558014.

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Goswami, Sivaranjan, Kandarpa Kumar Sarma, and Kumaresh Sarmah. "Synthesis of a Sparse 2D-Scanning Array using Particle Swarm Optimization for Side-Lobe Reduction." WSEAS TRANSACTIONS ON COMMUNICATIONS 20 (August 10, 2021): 112–16. http://dx.doi.org/10.37394/23204.2021.20.14.

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Synthesis of sparse arrays is a promising area of research for a wide range of applications including radar and millimeter-wave wireless communication. The design goal of array thinning problems is to reduce the number of elements of an array without significantly affecting its performance. This work presents a technique for synthesizing a sparse phased-array antenna from a 16×16 uniform rectangular array (URA). The proposed approach reduces the number of elements by 50% without any significant increase in the peak sidelobe level (PSLL) for all possible scan angles in the azimuthal and elevation plans within a finite range of scan angles. The synthesis includes an artificial neural network (ANN) model for estimation of the excitation weights of the URA for a given scan-angle. The weights of the sparse array are computed by the Hadamard product of the weight matrix of the URA with a binary matrix that is obtained using particle swarm optimization (PSO) to minimize the PSLL.

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Gorbachev, A. P., and Yu N. Parshin. "PRINTED DIPOLE 8-BEAM ANTENNA ARRAY WITH CHART-FORMING MATRIX BUTLER ON CONNECTED STRIP LINES." Issues of radio electronics, no. 4 (May 10, 2019): 65–70. http://dx.doi.org/10.21778/2218-5453-2019-4-65-70.

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The results of the analysis of the printed 8-beam antenna array with an operating frequency of 2.2 GHz are presented. Charting devices made on the basis of the Butler matrix are widely used in modern telecommunication and radar systems and complexes. Forming in space a fan of independent radiation patterns, such arrays provide both scanning in azimuth and elevation and switching several transmitters to different directions. This ensures a high efficiency of a multipath planar phased antenna array, and reduces its overall dimensions. To test the features of the implementation of the 8-input Butler matrix, an eight-beam dipole phased array antenna was modeled. The results of the system analysis and computer simulation showed that it is possible to print the antenna with acceptable performance.

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Gau, Hwa-Long, and Pei Yuan Wu. "Zero-dilation Index of S_n-matrix and Companion Matrix." Electronic Journal of Linear Algebra 31 (February 5, 2016): 666–78. http://dx.doi.org/10.13001/1081-3810.3193.

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The zero-dilation index $d(A)$ of a square matrix $A$ is the largest $k$ for which $A$ is unitarily similar to a matrix of the form ${\scriptsize\left[\begin{array}{cc} 0_k & \ast\\ \ast & \ast\end{array}\right]}$, where $0_k$ denotes the $k$-by-$k$ zero matrix. In this paper, it is shown that if $A$ is an $S_n$-matrix or an $n$-by-$n$ companion matrix, then $d(A)$ is at most $\lceil n/2\rceil$, the smallest integer greater than or equal to $n/2$. Those $A$'s for which the upper bound is attained are also characterized. Among other things, it is shown that, for an odd $n$, the $S_n$-matrix $A$ is such that $d(A)=(n+1)/2$ if and only if $A$ is unitarily similar to $-A$, and, for an even $n$, every $n$-by-$n$ companion matrix $A$ has $d(A)$ equal to $n/2$

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Kurganov, Vladislav V., and Victor I. Djigan. "Digital antenna array calibration by adaptive algorithms of signal processing." Telecommunications, no. 2 (2021): 8–16. http://dx.doi.org/10.31044/1684-2588-2021-0-2-8-1.

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A method of antenna array calibration, based on the using of the recursive least squares (RLS) adaptive filtering algorithms is discussed. The matrix inversion lemma, the QR-decomposition and the Householder transform based RLS algorithms with quadratic computational complexity can be used for the method implementation in the narrowband adaptive arrays. The proposed calibration method is used in the antenna arrays with digital beam forming. The method demonstrates the ability to estimate and compensate the channel gain errors that ensures the average deviation of the calibrated array radiation pattern shape relatively the radiation pattern shape of the ideal array about –20 dB.

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Guo, Chenxi, Xinhong Hao, and Ping Li. "An Improved Trilinear Model-Based Angle Estimation Method for Co-Prime Planar Arrays." Sensors 18, no. 12 (November 28, 2018): 4180. http://dx.doi.org/10.3390/s18124180.

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Angle estimation methods in two-dimensional co-prime planar arrays have been discussed mainly based on peak searching and sparse recovery. Peak searching methods suffer from heavy computational complexity and sparse recovery methods face some problems in selecting the regularization parameters. In this paper, we propose an improved trilinear model-based method for angle estimation for co-prime planar arrays in the view of trilinear decomposition, namely parallel factor analysis. Due to the principle of trilinear decomposition, our method does not require peak searching and can conduct auto-pairing easily, which can reduce the computational loads and avoid parameter selection problems. Furthermore, we exploit the virtual array concept of the whole co-prime planar array through the cross-correlation matrix obtained from the received signal data and present a matrix reconstruction method using the Khatri–Rao product to tackle the matrix rank deficiency problem in the virtual array condition. The simulation results show that our proposed method can not only achieve high estimation accuracy with low complexity compared to other similar approaches, but also utilize limited sensor number to implement the angle estimation tasks.

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Hassan, Tehseen, Fei Gao, Babur Jalal, and Sheeraz Arif. "Direction of Arrival Estimation Using Augmentation of Coprime Arrays." Information 9, no. 11 (November 9, 2018): 277. http://dx.doi.org/10.3390/info9110277.

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Recently, direction of arrival (DOA) estimation premised on the sparse arrays interpolation approaches, such as co-prime arrays (CPA) and nested array, have attained extensive attention because of the effectiveness and capability of providing higher degrees of freedom (DOFs). The co-prime array interpolation approach can detect O(MN) paths with O(M + N) sensors in the array. However, the presence of missing elements (holes) in the difference coarray has limited the number of DOFs. To implement co-prime coarray on subspace based DOA estimation algorithm namely multiple signal classification (MUSIC), a reshaping operation followed by the spatial smoothing technique have been presented in the literature. In this paper, an active coarray interpolation (ACI) is proposed to efficiently recovering the covariance matrix of the augmented coarray from the original covariance matrix of source signals with no vectorizing and spatial smoothing operation; thus, the computational complexity reduces significantly. Moreover, the numerical simulations of the proposed ACI approach offers better performance compared to its counterparts.

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Hucumenoglu, Mehmet, and Piya Pal. "Effect of Sparse Array Geometry on Estimation of Co-array Signal Subspace." Applied Computational Electromagnetics Society 35, no. 11 (February 5, 2021): 1435–36. http://dx.doi.org/10.47037/2020.aces.j.351186.

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This paper considers the effect of sparse array geometry on the co-array signal subspace estimation error for Direction-of-Arrival (DOA) estimation. The second largest singular value of the signal covariance matrix plays an important role in controlling the distance between the true subspace and its estimate. For a special case of two closely-spaced sources impinging on the array, we explicitly compute the second largest singular value of the signal covariance matrix and show that it can be significantly larger for a nested array when compared against a uniform linear array with same number of sensors.

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Cai, Chang-Xin, Guan-Jun Huang, Fang-Qing Wen, Xin-Hai Wang, and Lin Wang. "2D-DOA Estimation for EMVS Array with Nonuniform Noise." International Journal of Antennas and Propagation 2021 (August 18, 2021): 1–9. http://dx.doi.org/10.1155/2021/9053864.

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Electromagnetic vector sensor (EMVS) array is one of the most potential arrays for future wireless communications and radars because it is capable of providing two-dimensional (2D) direction-of-arrival (DOA) estimation as well as polarization angles of the source signal. It is well known that existing subspace algorithm cannot directly be applied to a nonuniform noise scenario. In this paper, we consider the 2D-DOA estimation issue for EMVS array in the presence of nonuniform noise and propose an improved subspace-based algorithm. Firstly, it recasts the nonuniform noise issue as a matrix completion problem. The noiseless array covariance matrix is then recovered via solving a convex optimization problem. Thereafter, the shift invariant principle of the EMVS array is adopted to construct a normalized polarization steering vector, after which 2D-DOA is easily estimated as well as polarization angles by incorporating the vector cross-product technique and the pseudoinverse method. The proposed algorithm is effective to EMVS array with arbitrary sensor geometry. Furthermore, the proposed algorithm is free from the nonuniform noise. Several simulations verify the improvement of the proposed method.

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Xiao, Yu, Tao Wu, Yiwen Li, Xinping Ma, and Yijie Huang. "Direction-of-Arrival Estimation for 2D Coherently Distributed Sources with Nested Array Based on Matrix Reconstruction." Mathematical Problems in Engineering 2020 (May 12, 2020): 1–13. http://dx.doi.org/10.1155/2020/6494967.

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This paper has made proposition of a nested array and an estimation algorithm for direction-of-arrival (DOA) of two-dimensional (2D) coherently distributed (CD) sources. According to the difference coarray concept, double parallel hole-free virtual uniform linear arrays are generated by virtue of vectorization operation on cross-correlation matrices of subarrays. Sensor coordinates of virtual arrays are derived. Rational invariance relationships of virtual arrays are derived. According to the rotational invariance relationships, matrices satisfying rotation invariance are constructed by extracting and regrouping the receive vectors of the virtual arrays, and then an estimation of signal parameters via rotational invariance techniques- (ESPRIT-) like framework on matrix reconstruction is deduced. Optimal configuration of the nested array as well as computational complexity are analyzed. Without pair matching, the proposed method can resolve more sources than the sensor number. Simulation outcomes indicate that the proposed method tends to have a better performance as compared to the traditional uniform arrays that have similar number of sensors.

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