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Journal articles on the topic 'Array processing'

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Published: 4 June 2021

Last updated: 25 May 2024

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1

Al‐Kurd, Azmi A., and Robert P. Porter. "Holographic array processing using truncated arrays." Journal of the Acoustical Society of America 93, no. 4 (April 1993): 2373. http://dx.doi.org/10.1121/1.406125.

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2

Sun, Muye, and Tianyu Duanmu. "DOA estimation technology based on array signal processing nested array." Applied and Computational Engineering 64, no. 1 (May 15, 2024): 23–29. http://dx.doi.org/10.54254/2755-2721/64/20241345.

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Research on non-uniform arrays has always been a focus of attention for scholars both domestically and internationally. Part of the research concentrates on existing non-uniform arrays, while another part focuses on optimizing the position of array elements or expanding the structure. Of course, there are also studies on one-dimensional and two-dimensional DOA estimation algorithms based on array spatial shapes, despite some issues. As long as there is a demand for spatial domain target positioning, the development and refinement of non-uniform arrays will continue to be a hot research direction. Nested arrays represent a unique type of heterogeneous array, whose special geometric shape significantly increases degrees of freedom and enhances estimation performance for directional information of undetermined signal sources. Compared to other algorithms, the one-dimensional DOA estimation algorithm based on spatial smoothing simplifies algorithm complexity, improves estimation accuracy under nested arrays, and can effectively handle the estimation of signal sources under uncertain conditions. The DFT algorithm it employs not only significantly improves angular estimation performance but also reduces operational complexity, utilizing full degrees of freedom to minimize aperture loss. Furthermore, the DFT-MUSIC method greatly reduces algorithmic computational complexity while performing very closely to the spatial smoothing MUSIC algorithm. The sparse arrays it utilizes, including minimum redundancy arrays, coprime arrays, and nested arrays, are a new type of array. Sparse arrays can increase degrees of freedom compared to traditional uniform linear arrays and solve the estimation of signal source angles under uncertain conditions, while also enhancing algorithm angular estimation performance.

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3

Fricke, J. Robert. "Serpentine array processing." Journal of the Acoustical Society of America 94, no. 3 (September 1993): 1867. http://dx.doi.org/10.1121/1.407612.

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4

Gerstoft, Peter, Katherine Kim, David Battle, W. A. Kuperman, William Hodgkiss, and Heechun Song. "Nonexhaustive array processing." Journal of the Acoustical Society of America 113, no. 4 (April 2003): 2264. http://dx.doi.org/10.1121/1.4780496.

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5

Long, D. "Array signal processing." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 5 (October 1985): 1346. http://dx.doi.org/10.1109/tassp.1985.1164669.

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6

Escudié, Bernhard. "Array signal processing." Signal Processing 10, no. 3 (April 1986): 325–26. http://dx.doi.org/10.1016/0165-1684(86)90113-1.

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7

Bjørnø, L. "Array signal processing." Ultrasonics 23, no. 6 (November 1985): 283. http://dx.doi.org/10.1016/0041-624x(85)90052-6.

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8

Jesshope, C. R. "Parallel Array Processing." Computer Physics Communications 43, no. 2 (January 1987): 313. http://dx.doi.org/10.1016/0010-4655(87)90215-3.

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9

Uthansaku, Monthippa, and Marek E. Bialkowski. "A wideband smart antenna employing spatial signal processing." Journal of Telecommunications and Information Technology, no. 1 (June 24, 2023): 13–17. http://dx.doi.org/10.26636/jtit.2007.1.743.

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A smart antenna with capability of beam steeringin azimuth over a wide frequency band using only spatial sig-nal processing is presented. Filters and tapped-delay networksemployed in conventional wideband linear arrays are avoidedby using a two-dimensional rectangular array structure. Inthis array, only constant real-valued weighting coefficients, re-alized with amplifiers or attenuators, are used to form a de-sired radiation pattern. In order to estimate direction of ar-rival of a wideband signal, the MUSIC algorithm in conjunc-tion with an interpolated array technique is applied. In theinterpolated array technique, a composite covariance matrixis generated, which is a simple addition of covariance matricesof narrowband virtual arrays, being stretched or compressedversions of a nominal array. A working prototype of this wide-band array is presented. Its operation is assessed via full EMsimulations and measurements.

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10

ma, fei, Sipei Zhao, and Thushara Abhayapala. "Physics-informed neural network assisted spherical microphone array signal processing." Journal of the Acoustical Society of America 154, no. 4_supplement (October 1, 2023): A182. http://dx.doi.org/10.1121/10.0023200.

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Thanks to their rotational symmetry that facilitates three-dimensional signal processing, spherical microphone arrays are the common array apertures used for spatial audio and acoustic applications. However, practical implementations of spherical microphone arrays suffer from two issues. First, at high frequency range, a large number of sensors are needed to accurately capture a sound field. Second, the accompanying signal processing algorithm, i.e., the spherical harmonic decomposition method, requires a variable radius array or a rigid surface array to circumvent the spherical Bessel function nulls. Such arrays are hard to design and introduce a scattering field. To address these issues, this paper proposes to assist a spherical microphone array with a physics-informed neural network (PINN) for three-dimensional signal processing. The PINN models the sound field around the array based on the sensor measurements and the acoustic wave equation, augmenting the sound field information captured by the array through prediction. This makes it possible to analyze a high frequency sound field with a reduced number of sensors and avoid the spherical Bessel function nulls with a simple single radius open-sphere microphone array.

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11

Debever, Claire, and W. A. Kuperman. "Broadband, coherent inter-array processing of horizontal arrays." Journal of the Acoustical Society of America 122, no. 5 (2007): 3022. http://dx.doi.org/10.1121/1.2942803.

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12

Hanna, M., and M. Simaan. "Absolutely optimum array filters for sensor arrays." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 6 (December 1985): 1380–86. http://dx.doi.org/10.1109/tassp.1985.1164726.

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13

Pivarsk, Jim, Jaydeep Nandi, David Lange, and Peter Elmer. "Columnar data processing for HEP analysis." EPJ Web of Conferences 214 (2019): 06026. http://dx.doi.org/10.1051/epjconf/201921406026.

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In the last stages of data analysis, physicists are often forced to choose between simplicity and execution speed. In High Energy Physics (HEP), high-level languages like Python are known for ease of use but also very slow execution. However, Python is used in speed-critical data analysis in other fields of science and industry. In those fields, most operations are performed on Numpy arrays in an array programming style; this style can be adopted for HEP by introducing variable-sized, nested data structures. We describe how array programming may be extended for HEP use-cases and an implementation known as awkward-array. We also present integration with ROOT, Apache Arrow, and Parquet, as well as preliminary performance results.

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14

Holmes, Neville. "Simplifying array processing languages." ACM SIGAPL APL Quote Quad 29, no. 3 (March 1999): 91–96. http://dx.doi.org/10.1145/327600.327621.

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15

Martinez-Ramon, Manel, Jose Luis Rojo-Alvarez, Gustavo Camps-Valls, and Christos G. Christodoulou. "Kernel Antenna Array Processing." IEEE Transactions on Antennas and Propagation 55, no. 3 (March 2007): 642–50. http://dx.doi.org/10.1109/tap.2007.891550.

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16

Brookner, E., and J. M. Howell. "Adaptive-adaptive array processing." Proceedings of the IEEE 74, no. 4 (1986): 602–4. http://dx.doi.org/10.1109/proc.1986.13507.

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17

Cheng-Yuan Liou and Ruey-Ming Liou. "Spatial pseudorandom array processing." IEEE Transactions on Acoustics, Speech, and Signal Processing 37, no. 9 (1989): 1445–49. http://dx.doi.org/10.1109/29.31300.

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18

Gabriel, W. F. "Adaptive processing array systems." Proceedings of the IEEE 80, no. 1 (1992): 152–62. http://dx.doi.org/10.1109/5.119574.

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19

LECHTCHINSKY, ROMAN, MANUEL M. T. CHAKRAVARTY, and GABRIELE KELLER. "COSTING NESTED ARRAY CODES." Parallel Processing Letters 12, no. 02 (June 2002): 249–66. http://dx.doi.org/10.1142/s0129626402000951.

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We discuss a language-based cost model for array programs built on the notions of work complexity and parallel depth. The programs operate over data structures comprising nested arrays and recursive product-sum types. In a purely functional setting, such programs can be implemented by way of the flattening transformation that converts codes over nested arrays into vectorised code over flat arrays. Flat arrays lend themselves to a particularly efficient implementation on standard hardware, but the overall efficiency of the approach depends on the flattening transformation preserving the asymptotic complexity of the nested array codes. Blelloch has characterised a class of first-order array programs, called contained programs, for which flattening preserves the asymptotic depth complexity. However, his result is restricted to programs processing only arrays and tuples. In the present paper, we extend Blelloch's result to array programs processing data structures containing arrays as well as arbitrary recursive product-sum types. Moreover, we replace the notion of containment by the more general concept of fold programs.

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20

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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21

Galli-Resta, Lucia, Elena Novelli, and Alessandro Viegi. "Dynamic microtubule-dependent interactions position homotypic neurones in regular monolayered arrays during retinal development." Development 129, no. 16 (August 15, 2002): 3803–14. http://dx.doi.org/10.1242/dev.129.16.3803.

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In the vertebrate retina cell layers support serial processing, while monolayered arrays of homotypic neurones tile each layer to allow parallel processing. How neurones form layers and arrays is still largely unknown. We show that monolayered retinal arrays are dynamic structures based on dendritic interactions between the array cells. The analysis of three developing retinal arrays shows that these become regular as a net of dendritic processes links neighbouring array cells. Molecular or pharmacological perturbations of microtubules within dendrites lead to a stereotyped and reversible disruption of array organization: array cells lose their regular spacing and the arrangement in a monolayer. This leads to a micro-mechanical explanation of how monolayers of regularly spaced ‘like-cells’ are formed.

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22

Hsu, Kai, and Shu‐Kong Chang. "Multiple‐shot processing of array sonic waveforms." GEOPHYSICS 52, no. 10 (October 1987): 1376–90. http://dx.doi.org/10.1190/1.1442250.

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Signal processing techniques are needed to estimate the slownesses (inverses of velocities) of compressional, shear, and Stoneley waves from array sonic waveforms recorded by array sonic tools. Existing processing techniques process entire array waveforms, assuming that each wave component has linear moveout (a constant velocity) across the array. The results show poor vertical resolution if the formation spanned by the array aperture is not homogeneous. Multiple‐shot processing is a technique to improve the vertical resolution of slowness logs. The algorithm incorporates redundant information from the multiplicity of overlapping tool locations. Specifically, the method includes four steps: (1) subarrays are selected from full arrays associated with shots from successive sources; (2) subarray waveforms are processed using the semblance statistic; (3) the semblance statistic is projected onto the slowness axis; and (4) the projected semblances of all selected subarrays are combined and the slownesses of the wave components are estimated from the combined statistic. The technique improves the vertical resolution of slowness logs because the subarrays are shorter than the full array. It also increases the accuracy of estimates of slowness because more data are used in the processing. This procedure is applicable to common‐shot subarrays as well as to common‐receiver subarrays. Combining common‐shot and common‐receiver results provides two additional advantages, namely, borehole compensation and measurement enhancement.

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23

Bazurin, Vitalii M. "МЕТОДИКА НАВЧАННЯ УЧНІВ РОЗВ’ЯЗУВАННЯ ЗАДАЧ З ОПРАЦЮВАННЯ МАСИВІВ У СЕРЕДОВИЩІ ВІЗУАЛЬНОГО ПРОГРАМУВАННЯ DELPHI." Information Technologies and Learning Tools 47, no. 3 (May 14, 2015): 25. http://dx.doi.org/10.33407/itlt.v47i3.1190.

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Delphi visual programming environment provides ample opportunities for visual mapping arrays. There are a number of Delphi screen form components, which help you to visualize the array on the form. Processing arrays programs in Delphi environment have their differences from the same programs in Pascal. The article describes these differences. Also, the features of student learning methods for solving problems of array processing using Delphi visual components are highlighted. It has been exposed sequence and logic of the teaching material on arrays processing using TStringGrid and TMemo components.

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24

Patwari, Ashish, and G. Ramachandra Reddy. "A Conceptual Framework for the Use of Minimum Redundancy Linear Arrays and Flexible Arrays in Future Smartphones." International Journal of Antennas and Propagation 2018 (September 18, 2018): 1–12. http://dx.doi.org/10.1155/2018/9629837.

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This work applies existing array processing principles to devise a new area of application. The properties of minimum redundancy linear arrays (MRLAs) and flexible arrays are studied, keeping in mind the possibility of using them in flexible 5G smartphones of the future. Millimeter frequencies for 5G communications enabled the use of a decent number of array elements, even at the user equipment (UE). MRLAs possess attractive properties among linear sparse arrays and flexible conformal arrays (flexible arrays) operate satisfactorily even when the surface they are built into changes shape. To the best of our knowledge, MRLAs were not applied to smartphones previously. In this work, a 16-element uniform linear array (ULA) and a 7-element MRLA (with the same aperture) are considered for simulations. Array factors of both the arrays in flat and bent positions have been computed using MATLAB. The effect of phase compensation and bending radii on the array pattern were verified. That phase compensation using the projection method (PM) restores the array pattern even for a bent MRLA is a major finding. Possible array processing modes have been suggested for a 5G smartphone in which the array could be made to operate in any of the four configurations: a flat ULA, a bent ULA, a flat MRLA, and a bent MRLA.

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25

Trickey, Stephen, Kirk Daley, Alvaro Bautista, and Clay Kirkendall. "A practical spatially oversampled array concept enabling reconfigurability." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A135. http://dx.doi.org/10.1121/10.0010895.

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Typically, acoustic arrays are cut for a desired performance characteristic where acoustic performance is balanced with physical and cost constraints. Once constructed, the sensor placement in the array is fixed in a permanent configuration. Adaptive processing techniques can be used to optimize the acoustic characteristics of the array but are bounded by the fixed sensor placement. Reconfigurable arrays, where the sensor locations and apertures can be dynamically adjusted, can offer improved acoustic performance and signal processing power savings for a given array aperture. This work presents a new concept for fiber optic hydrophone arrays that uses a highly spatially oversampled multiplexing strategy enabling a large degree of flexibility in sensing configuration. The ease of multiplexing optical hydrophones allows for implementing the oversampled strategy with no additional processing hardware required. A review of fiber optic acoustic sensing and the optical techniques required to implement high performance spatially oversampled arrays will be presented. [DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.]

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26

Frikel, Miloud, and Salah Bourennane. "Wideband array processing : Focusing operators." Annales Des Télécommunications 52, no. 5-6 (May 1997): 339–48. http://dx.doi.org/10.1007/bf02996076.

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27

Le Cadre, Jean‐Pierre, and Olivier Zugmeyer. "Temporal integration for array processing." Journal of the Acoustical Society of America 93, no. 3 (March 1993): 1471–81. http://dx.doi.org/10.1121/1.406805.

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28

Cox, H. "Array processing and underwater acoustics." Journal of the Acoustical Society of America 78, S1 (November 1985): S29. http://dx.doi.org/10.1121/1.2022734.

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29

Moses, R. L., and A. A. Beex. "Instrumental variable adaptive array processing." IEEE Transactions on Aerospace and Electronic Systems 24, no. 2 (March 1988): 192–202. http://dx.doi.org/10.1109/7.1053.

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30

Eichmann, George, Yao Li, Ping Pei Ho, and R. R. Alfano. "Digital optical isochronous array processing." Applied Optics 26, no. 14 (July 15, 1987): 2726. http://dx.doi.org/10.1364/ao.26.002726.

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31

Sullivan, Edmund J. "Model‐based towed array processing." Journal of the Acoustical Society of America 101, no. 5 (May 1997): 3156. http://dx.doi.org/10.1121/1.419142.

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32

Nehorai, A., and E. Paldi. "Acoustic vector-sensor array processing." IEEE Transactions on Signal Processing 42, no. 9 (1994): 2481–91. http://dx.doi.org/10.1109/78.317869.

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33

Thompson, J. S. "Editorial: Antenna array processing techniques." IEE Proceedings - Radar, Sonar and Navigation 145, no. 1 (1998): 1. http://dx.doi.org/10.1049/ip-rsn:19981818.

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34

Singh, Hema, and Rakesh Mohan Jha. "Trends in Adaptive Array Processing." International Journal of Antennas and Propagation 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/361768.

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Enormous progress has been made during the past five decades in the area of adaptive array processing. Increased computational power has resulted in many practical applications of optimum algorithms. The present paper deals with many facets of array signal processing and adaptive beam forming. It provides a comprehensive description of various beam-forming schemes, adaptive algorithms to adjust the required weighting on antenna elements, direction-of-arrival estimation methods, including their performance comparison. The effects of various types of errors on the performance of an array system are illustrated along with their remedial measures. Since array signal processing has widespread applications, the study is carried out across various disciplines.

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35

Berardi, William. "ACOUSTIC TRANSDUCER ARRAY SIGNAL PROCESSING." Journal of the Acoustical Society of America 131, no. 4 (2012): 3190. http://dx.doi.org/10.1121/1.4707436.

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36

Liao, Bin, Arjuna Madanayake, and Panajotis Agathoklis. "Array signal processing and systems." Multidimensional Systems and Signal Processing 29, no. 2 (February 15, 2018): 467–73. http://dx.doi.org/10.1007/s11045-018-0555-7.

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37

Distante, Fausto. "Parallel processing: Array processors I." Microprocessing and Microprogramming 24, no. 1-5 (August 1988): 627. http://dx.doi.org/10.1016/0165-6074(88)90121-4.

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38

Weiss, Anthony J., and Benjamin Friedlander. "Array processing using joint diagonalization." Signal Processing 50, no. 3 (May 1996): 205–22. http://dx.doi.org/10.1016/0165-1684(96)00021-7.

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39

Foster, Scott. "Low Sidelobe Sparse Array Processing." Digital Signal Processing 12, no. 2-3 (January 2002): 360–71. http://dx.doi.org/10.1006/dspr.2002.0455.

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40

Boué, Pierre, Philippe Roux, Michel Campillo, and Benoit de Cacqueray. "Double beamforming processing in a seismic prospecting context." GEOPHYSICS 78, no. 3 (May 1, 2013): V101—V108. http://dx.doi.org/10.1190/geo2012-0364.1.

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The use of larger numbers of sensors is becoming more common at the large, continental scale for deep-structure imaging in seismology, and at a smaller scale with exploration geophysics objectives. Seismic arrays require array processing from which new types of observables contribute to a better understanding of the wave propagation complexity. From among these array processing techniques, this study focuses on a way to select and identify different phases between two source-receiver arrays based on the double beamforming (DBF) method. At the exploration geophysics scale, the goal is to identify and separate low-amplitude body waves from high-amplitude dispersive surface waves. A synthetic data set from a finite-difference time-domain simulation is first used to validate the array processing method. From directional information obtained with DBF, and due to the double-plane wave projection, it is demonstrated that surface and body waves can be extracted with a higher efficacy compared to classical beamforming even at short offset. A seismic prospecting data set in a laterally heterogeneous medium is then investigated. This data set is a high-resolution survey which provides a perfect control on source and receiver arrays geometry. The separation between the direct surface and body waves is observed after DBF and ray bending is discussed from the additional azimuthal information.

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41

Hudson, Thomas Samuel, Alex M. Brisbourne, Sofia-Katerina Kufner, J. Michael Kendall, and Andy M. Smith. "Array processing in cryoseismology: a comparison to network-based approaches at an Antarctic ice stream." Cryosphere 17, no. 11 (November 27, 2023): 4979–93. http://dx.doi.org/10.5194/tc-17-4979-2023.

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Abstract. Seismicity at glaciers, ice sheets, and ice shelves provides observational constraint on a number of glaciological processes. Detecting and locating this seismicity, specifically icequakes, is a necessary first step in studying processes such as basal slip, crevassing, imaging ice fabric, and iceberg calving, for example. Most glacier deployments to date use conventional seismic networks, comprised of seismometers distributed over the entire area of interest. However, smaller-aperture seismic arrays can also be used, which are typically sensitive to seismicity distal from the array footprint and require a smaller number of instruments. Here, we investigate the potential of arrays and array-processing methods to detect and locate subsurface microseismicity at glaciers, benchmarking performance against conventional seismic-network-based methods for an example at an Antarctic ice stream. We also provide an array-processing recipe for body-wave cryoseismology applications. Results from an array and a network deployed at Rutford Ice Stream, Antarctica, show that arrays and networks both have strengths and weaknesses. Arrays can detect icequakes from further distances, whereas networks outperform arrays in more comprehensive studies of a particular process due to greater hypocentral constraint within the network extent. We also gain new insights into seismic behaviour at the Rutford Ice Stream. The array detects basal icequakes in what was previously interpreted to be an aseismic region of the bed, as well as new icequake observations downstream and at the ice stream shear margins, where it would be challenging to deploy instruments. Finally, we make some practical recommendations for future array deployments at glaciers.

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42

Safi, Alamgir, Muhammad Asghar Khan, Muhammad Adnan Aziz, Mohammed H. Alsharif, Tanweer Ahmad Cheema, Insaf Ullah, Abu Jahid, Abdulaziz H. Alghtani, and Ayman A. Aly. "Application of Differential Geometry to the Array Manifolds of Linear Arrays in Antenna Array Processing." Electronics 10, no. 23 (November 28, 2021): 2964. http://dx.doi.org/10.3390/electronics10232964.

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This article deals with the application of differential geometry to the array manifolds of non-uniform linear antenna array (NULA) when estimating the direction of arrival (DOA) of multiple sources present in an environment using far field approximation. In order to resolve this issue, we utilized a doublet linear antenna array (DLA) comprising two individual NULAs, along with a proposed algorithm that chooses correct directions of the impinging sources with the help of the prior knowledge of the ambiguous directions calculated with the application of differential geometry to the manifold curves of each NULA. The algorithm checks the correlation of the estimated direction of arrival (DOAs) by both the individual NULA with its corresponding ambiguous set of directions and chooses the output of the NULA, which has a minimum correlation between their estimated DOAs and corresponding ambiguous DOAs. DLA is designed such that the intersection of all the ambiguous set of DOAs among the individual NULAs are null sets. DOA of sources, which imping signals from different directions on the DLA, are estimated using three direction finding (DF) techniques, such as, genetic algorithm (GA), pattern search (PS), and a hybrid technique that utilizes both GA and PS at the same time. As compared to the existing techniques of ambiguity resolution, the proposed algorithm improves the estimation accuracy. Simulation results for all the three DF techniques utilizing the DLA along with the proposed algorithm are presented using MATLAB. As compared to the genetic algorithm and pattern search, the intelligent hybrid technique, such that, GA–PS, had better estimation accuracy in choosing corrected DOAs, despite the fact that the impinging DOAs were from ambiguous directions.

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43

GEORGE, ALAN D., JESUS GARCIA, KEONWOOK KIM, and PRIYABRATA SINHA. "DISTRIBUTED PARALLEL PROCESSING TECHNIQUES FOR ADAPTIVE SONAR BEAMFORMING." Journal of Computational Acoustics 10, no. 01 (March 2002): 1–23. http://dx.doi.org/10.1142/s0218396x02000511.

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Quiet submarine threats and high clutter in the littoral environment increase computation and communication demands on beamforming arrays, particularly for applications that require in-array autonomous operation. By coupling each transducer node in a distributed array with a microprocessor, and networking them together, embedded parallel processing for adaptive beamformers can glean advantages in execution speed, fault tolerance, scalability, power, and cost. In this paper, a novel set of techniques for the parallelization of adaptive beamforming algorithms is introduced for in-array sonar signal processing. A narrowband, unconstrained, Minimum Variance Distortionless Response (MVDR) beamformer is used as a baseline to investigate the efficiency and effectiveness of this method in an experimental fashion. Performance results are also included, among them execution times, parallel efficiencies, and memory requirements, using a distributed system testbed comprised of a cluster of workstations connected by a conventional network.

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44

Gibbons, Steven J. "The Applicability of Incoherent Array Processing to IMS Seismic Arrays." Pure and Applied Geophysics 171, no. 3-5 (October 17, 2012): 377–94. http://dx.doi.org/10.1007/s00024-012-0613-2.

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45

Li, Ting, Yi Zhang, and Liufang Fu. "Four-element planar arrays focus a point-like source based on the artificial iterative phase conjugated processing." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 3 (February 1, 2023): 4718–24. http://dx.doi.org/10.3397/in_2022_0682.

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The artificial iterative phase conjugated processing is an improved algorithm of phase conjugation and has been proven to focus a sharp focal spot using linear array. Because cross-shaped four-element planar array and triangular four-element planar array are widely used in the situation of little acoustic measuring points , their focal patterns by the artificial iterative phase conjugated processing are discussed in this paper. Theory analysis and numerical simulations gives conclusions. Based on the artificial iterative phase conjugated processing, two arrays focus a smaller focal spot than that by phase conjugation. As their iterate number increases, focal spot size decreases but the sidelobe amplitude becomes big. Considering the focal spot size and sidelobe interference, the triangular four-element array has a clearer pattern than the cross-shaped one .

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46

NARAYANAN, P. J., and LARRY S. DAVIS. "REPLICATED IMAGE ALGORITHMS AND THEIR ANALYSES ON SIMD MACHINES." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 02n03 (August 1992): 335–52. http://dx.doi.org/10.1142/s0218001492000217.

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Data parallel processing on processor array architectures has gained popularity in data intensive applications, such as image processing and scientific computing, as massively parallel processor array machines became feasible commercially. The data parallel paradigm of assigning one processing element to each data element results in an inefficient utilization of a large processor array when a relatively small data structure is processed on it. The large degree of parallelism of a massively parallel processor array machine does not result in a faster solution to a problem involving relatively small data structures than the modest degree of parallelism of a machine that is just as large as the data structure. We presented data replication technique to speed up the processing of small data structures on large processor arrays. In this paper, we present replicated data algorithms for digital image convolutions and median filtering, and compare their performance with conventional data parallel algorithms for the same on three popular array interconnection networks, namely, the 2-D mesh, the 3-D mesh, and the hypercube.

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Rao, Wei, Dan Li, and Jian Zhang. "A Novel PARAFAC Model for Processing the Nested Vector-Sensor Array." Sensors 18, no. 11 (October 31, 2018): 3708. http://dx.doi.org/10.3390/s18113708.

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In this paper, a novel parallel factor (PARAFAC) model for processing the nested vector-sensor array is proposed. It is first shown that a nested vector-sensor array can be divided into multiple nested scalar-sensor subarrays. By means of the autocorrelation matrices of the measurements of these subarrays and the cross-correlation matrices among them, it is then demonstrated that these subarrays can be transformed into virtual scalar-sensor uniform linear arrays (ULAs). When the measurement matrices of these scalar-sensor ULAs are combined to form a third-order tensor, a novel PARAFAC model is obtained, which corresponds to a longer vector-sensor ULA and includes all of the measurements of the difference co-array constructed from the original nested vector-sensor array. Analyses show that the proposed PARAFAC model can fully use all of the measurements of the difference co-array, instead of its partial measurements as the reported models do in literature. It implies that all of the measurements of the difference co-array can be fully exploited to do the 2-D direction of arrival (DOA) and polarization parameter estimation effectively by a PARAFAC decomposition method so that both the better estimation performance and slightly improved identifiability are achieved. Simulation results confirm the efficiency of the proposed model.

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Ata, Serdar Ozgur, and Cevdet Isik. "High-Resolution Direction-of-Arrival Estimation via Concentric Circular Arrays." ISRN Signal Processing 2013 (March 28, 2013): 1–8. http://dx.doi.org/10.1155/2013/859590.

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Estimating the direction of arrival (DOA) of source signals is an important research interest in application areas including radar, sonar, and wireless communications. In this paper, the problem of DOA estimation is addressed on concentric circular antenna arrays (CCA) in detail as an alternative to the well-known geometries of the uniform linear array (ULA) and uniform circular array (UCA). We define the steering matrix of the CCA geometry and investigate the performance analysis of the array in the DOA-estimation problem by simulations that are realized through varying the parameters of signal-to-noise ratio, number of sensors, and resolution angle of sensor arrays by using the MUSIC (Multiple Signal Classification) algorithm. The results present that CCA geometries provide higher angle resolutions compared to UCA geometries and require less physical area for the same number of sensor elements. However, as a cost-increasing effect, higher computational power is needed to estimate the DOA of source signals in CCAs compared to ULAs.

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Hu, Hang. "Aspects of the Subarrayed Array Processing for the Phased Array Radar." International Journal of Antennas and Propagation 2015 (2015): 1–21. http://dx.doi.org/10.1155/2015/797352.

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This paper gives an overview on the research status, developments, and achievements of subarrayed array processing for the multifunction phased array radar. We address some issues concerning subarrayed adaptive beamforming, subarrayed fast-time space-time adaptive processing, subarray-based sidelobe reduction of sum and difference beam, subarrayed adapted monopulse, subarrayed superresolution direction finding, subarray configuration optimization in ECCM (electronic counter-countermeasure), and subarrayed array processing for MIMO-PAR. In this review, several viewpoints relevant to subarrayed array processing are pointed out and the achieved results are demonstrated by numerical examples.

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Dubrovinskaya, Elizaveta, Veronika Kebkal, Oleksiy Kebkal, Konstantin Kebkal, and Paolo Casari. "Underwater Localization via Wideband Direction-of-Arrival Estimation Using Acoustic Arrays of Arbitrary Shape." Sensors 20, no. 14 (July 10, 2020): 3862. http://dx.doi.org/10.3390/s20143862.

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Underwater sensing and remote telemetry tasks necessitate the accurate geo-location of sensor data series, which often requires underwater acoustic arrays. These are ensembles of hydrophones that can be jointly operated in order to, e.g., direct acoustic energy towards a given direction, or to estimate the direction of arrival of a desired signal. When the available equipment does not provide the required level of accuracy, it may be convenient to merge multiple transceivers into a larger acoustic array, in order to achieve better processing performance. In this paper, we name such a structure an “array of opportunity” to signify the often inevitable sub-optimality of the resulting array design, e.g., a distance between nearest array elements larger than half the shortest acoustic wavelength that the array would receive. The most immediate consequence is that arrays of opportunity may be affected by spatial ambiguity, and may require additional processing to avoid large errors in wideband direction of arrival (DoA) estimation, especially as opposed to narrowband processing. We consider the design of practical algorithms to achieve accurate detections, DoA estimates, and position estimates using wideband arrays of opportunity. For this purpose, we rely jointly on DoA and rough multilateration estimates to eliminate spatial ambiguities arising from the array layout. By means of emulations that realistically reproduce underwater noise and acoustic clutter, we show that our algorithm yields accurate DoA and location estimates, and in some cases it allows arrays of opportunity to outperform properly designed arrays. For example, at a signal-to-noise ratio of –20 dB, a 15-element array of opportunity achieves lower average and median localization error (27 m and 12 m, respectively) than a 30-element array with proper λ / 2 element spacing (33 m and 15 m, respectively). We confirm the good accuracy of our approach via emulation results, and through a proof-of-concept lake experiment, where our algorithm applied to a 10-element array of opportunity achieves a 90th-percentile DoA estimation error of 4 ∘ and a 90th-percentile total location error of 5 m when applied to a real 10-element array of opportunity.

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