Relevant bibliographies by topics / Array processing

Academic literature on the topic 'Array processing'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Array processing"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Array processing"

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Jafri, Ahsan. "Array signal processing based on traditional and sparse arrays." Thesis, University of Sheffield, 2019. http://etheses.whiterose.ac.uk/23072/.

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Array signal processing is based on using an array of sensors to receive the impinging signals. The received data is either spatially filtered to focus the signals from a desired direction or it may be used for estimating a parameter of source signal like direction of arrival (DOA), polarization and source power. Spatial filtering also known as beamforming and DOA estimation are integral parts of array signal processing and this thesis is aimed at solving some key probems related to these two areas. Wideband beamforming holds numerous applications in the bandwidth hungry data traffic of present day world. Several techniques exist to design fixed wideband beamformers based on traditional arrays like uniform linear array (ULA). Among these techniques, least squares based eigenfilter method is a key technique which has been used extensively in filter and wideband beamformer design. The first contribution of this thesis comes in the form of critically analyzing the standard eigenfilter method where a serious flaw in the design formulation is highlighted which generates inconsistent design performance, and an additional constraint is added to stabilize the achieved design. Simulation results show the validity and significance of the proposed method. Traditional arrays based on ULAs have limited applications in array signal processing due to the large number of sensors required and this problem has been addressed by the application of sparse arrays. Sparse arrays have been exploited from the perspective of their difference co-array structures which provide significantly higher number of degrees of freedoms (DOFs) compared to ULAs for the same number of sensors. These DOFs (consecutive and unique lags) are utilized in the application of DOA estimation with the help of difference co-array based DOA estimators. Several types of sparse arrays include minimum redundancy array (MRA), minimum hole array (MHA), nested array, prototype coprime array, conventional coprime array, coprime array with compressed interelement spacing (CACIS), coprime array with displaced subarrays (CADiS) and super nested array. As a second contribution of this thesis, a new sparse array termed thinned coprime array (TCA) is proposed which holds all the properties of a conventional coprime array but with $\ceil*{\frac{M}{2}}$ fewer sensors where $M$ is the number of sensors of a subarray in the conventional structure. TCA possesses improved level of sparsity and is robust against mutual coupling compared to other sparse arrays. In addition, TCA holds higher number of DOFs utilizable for DOA estimation using variety of methods. TCA also shows lower estimation error compared to super nested arrays and MRA with increasing array size. Although TCA holds numerous desirable features, the number of unique lags offered by TCA are close to the sparsest CADiS and nested array and significantly lower than MRA which limits the estimation error performance offered by TCA through (compressive sensing) CS-based methods. In this direction, the structure of TCA is studied to explore the possibility of an array which can provide significantly higher number of unique lags with improved sparsity for a given number of sensors. The result of this investigation is the third contribution of this thesis in the form of a new sparse array, displaced thinned coprime array with additional sensor (DiTCAAS), which is based on a displaced version of TCA. The displacement of the subarrays generates an increase in the unique lags but the minimum spacing between the sensors becomes an integer multiple of half wavelength. To avoid spatial aliasing, an additional sensor is added at half wavelength from one of the sensors of the displaced subarray. The proposed placement of the additional sensor generates significantly higher number of unique lags for DiTCAAS, even more than the DOFs provided by MRA. Due to its improved sparsity and higher number of unique lags, DiTCAAS generates the lowest estimation error and robustness against heavy mutual coupling compared to super nested arrays, MRA, TCA and sparse CADiS with CS-based DOA estimation.
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Otsuka, Takuma. "Bayesian Microphone Array Processing." 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/188871.

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Kyoto University (京都大学)
0048
新制・課程博士
博士(情報学)
甲第18412号
情博第527号
新制||情||93(附属図書館)
31270
京都大学大学院情報学研究科知能情報学専攻
(主査)教授 奥乃 博, 教授 河原 達也, 准教授 CUTURI CAMETO Marco, 講師 吉井 和佳
学位規則第4条第1項該当
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Alexiou, Angeliki. "Bounds in array processing." Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249378.

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Sabbar, Bayan M. "High resolution array signal processing." Thesis, Loughborough University, 1987. https://dspace.lboro.ac.uk/2134/27193.

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This study is concerned with the processing of signals received by an array of sensor elements which may range from acoustic transducers in a sonar system to microwave horns in a radar system. The main aim of the work is to devise techniques for resolving the signals arriving from closely spaced sources in order to determine the presence and direction of these sources.
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Kitchens, Jonathan Paul. "Acoustic vector-sensor array processing." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60098.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 145-148).
Existing theory yields useful performance criteria and processing techniques for acoustic pressure-sensor arrays. Acoustic vector-sensor arrays, which measure particle velocity and pressure, offer significant potential but require fundamental changes to algorithms and performance assessment. This thesis develops new analysis and processing techniques for acoustic vector-sensor arrays. First, the thesis establishes performance metrics suitable for vector sensor processing. Two novel performance bounds define optimality and explore the limits of vector-sensor capabilities. Second, the thesis designs non-adaptive array weights that perform well when interference is weak. Obtained using convex optimization, these weights substantially improve conventional processing and remain robust to modeling errors. Third, the thesis develops subspace techniques that enable near-optimal adaptive processing. Subspace processing reduces the problem dimension, improving convergence or shortening training time.
by Jonathan Paul Kitchens.
Ph.D.
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Schodorf, Jeffrey Brian. "Array processing techniques for interference suppression in mobile communications systems." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/12971.

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Hicks, James Edward. "Novel Approaches to Overloaded Array Processing." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/28670.

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An antenna array is overloaded when the number of cochannel signals in its operating environment exceeds the number of elements. Conventional space-time array processing for narrow-band signals fails in overloaded environments. Overloaded array processing (OLAP) is most difficult when signals impinging on the array are near equal power, have tight excess bandwidth, and are of identical signal type. Despite the failure of conventional beamforming in such environments, OLAP becomes possible when a receiver exploits additional signal properties such as the finite-alphabet property and signal excess-bandwidth. This thesis proposes three approaches to signal extraction in overloaded environments, each providing a different tradeoff in performance and complexity. The first receiver architecture extracts signals from an overloaded environment through the use of MMSE interference rejection filtering embedded in a successive interference cancellation (SIC) architecture. The second receiver architecture enhances signal extraction performance by embedding a stronger interference rejection receiver, the reduced-state maximum aposteriori probability (RS-MAP) algorithm in a similar SIC architecture. The third receiver fine-tunes the performance of spatially reduced search joint detection (SRSJD) with the application of an energy focusing transform (EFT), a complexity reducing front-end linear pre-processor. A new type of EFT, the Energy Focusing Unitary Relaxed Transform (EFURT) is developed. This transform facilitates a continuous tradeoff between noise-enhancement and error-propagation in an SRSJD framework. EFURT is used to study the role of this tradeoff for SRSJD receivers in a variety of signal environments. It is found that for the environments studied in this thesis, SRSJD enjoys an aggressive reduction in interference at the expense of possible noise-enhancement.
Ph. D.
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Karaminas, Panagiotis D. "Array processing in mobile radio networks." Thesis, Imperial College London, 2001. http://hdl.handle.net/10044/1/11483.

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Arnott, Robert. "Array processing for digital mobile radio." Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338780.

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Åhgren, Per. "Teleconferencing, system identification and array processing." Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-86013.

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The area of teleconferencing has long yielded great interest in the signal processing community. The main reasons for this are probably the huge interest from the industry and the challenging problems of the topic. The problems of teleconferencing are relevant for several different disciplines in signal processing. Three of these are Acoustic Echo Cancellation, System Identification and Sensor Array Signal Processing. In this thesis some problems related to these disciplines are studied. The thesis is divided into 6 parts, one for each paper included. In the first part a new adaptive algorithm is applied to the acoustic echo cancellation problem. It is shown to perform much better than the Normalized Least Mean Squares (NLMS) algorithm and while it performs worse than the standard Recursive Least Squares (RLS) algorithm it is shown to be computationally simpler than this. In the second part the hierarchical RLS algorithm is analyzed. The extraordinary results presented for this algorithm in previous papers are discussed and explained. In the third part a new initialization method for RLS is presented that yields the exact Least Squares estimates while not being computationally more demanding than RLS. This is an important contribution since the standard initialization of the RLS algorithm is somewhat arbitrary. In the fourth part a method is presented that deals with the problem of estimating the common factors out of an arbitrary number of polynomials. Two problems of array processing and system identification are stated as problems for common factor estimation and the presented method is applied to these. For these two problems the method is shown to perform better than existing methods. In the fifth part a method for beamforming using few sensors is presented. Data-dependent beamformers usually perform badly when there are few sensors in the array, particularly when the beamformer constraints are numerous. The method presented deals with this problem by approximately fulfilling the beamformer constraints and hence getting extra degrees of freedom for suppressing interferences. In the sixth part the previously unsolved problem of array processing of non-zero mean signals is solved for the colored noise case. Methods are presented both for the estimation problem and the detection problem and are shown to perform well in numerical examples.
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Books on the topic "Array processing"

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Benesty, Jacob, Israel Cohen, and Jingdong Chen. Array Processing. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15600-8.

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Yan, Shefeng. Broadband Array Processing. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6802-8.

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Pillai, S. Uṇṇikrishṇa, and C. S. Burrus, eds. Array Signal Processing. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3632-0.

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Haykin, Simon, John Litva, and Terence J. Shepherd, eds. Radar Array Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77347-1.

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Practical array processing. New York: McGraw-Hill, 2009.

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S, Burrus C., ed. Array signal processing. New York: Springer-Verlag, 1989.

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Pillai, S. Uṇṇikrishṇa. Array Signal Processing. New York, NY: Springer New York, 1989.

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1931-, Haykin Simon S., Litva J. 1937-, and Shepherd T. J. 1952-, eds. Radar array processing. Berlin: Springer-Verlag, 1993.

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1931-, Haykin Simon S., and Justice James H. 1941-, eds. Array signal processing. Englewood Cliffs, N.J: Prentice-Hall, 1985.

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Haykin, Simon. Radar Array Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993.

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Book chapters on the topic "Array processing"

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Gehrke, Wilhelm. "Array Processing." In The F Language Guide, 47–68. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0989-1_6.

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Gehrke, Wilhelm. "Array Processing." In Fortran 95 Language Guide, 55–76. London: Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-1025-5_6.

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Havskov, Jens, and Lars Ottemöller. "Array Processing." In Routine Data Processing in Earthquake Seismology, 283–97. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8697-6_9.

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Gehrke, Wilhelm. "Array Processing." In Fortran 90 Language Guide, 53–74. London: Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3014-7_6.

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van Leeuwen, J., and J. Wiedermann. "Array processing machines." In Fundamentals of Computation Theory, 257–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/bfb0028810.

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Schobben, Daniel W. E. "Array Processing Techniques." In Real-time Adaptive Concepts in Acoustics, 15–28. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0812-9_2.

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Jarrett, Daniel P., Emanuël A. P. Habets, and Patrick A. Naylor. "Parametric Array Processing." In Springer Topics in Signal Processing, 141–50. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42211-4_8.

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Thomas, Christine. "Array Signal Processing." In Handbook of Signal Processing in Acoustics, 1655–65. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-30441-0_92.

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Jarrett, Daniel P., Emanuël A. P. Habets, and Patrick A. Naylor. "Informed Array Processing." In Springer Topics in Signal Processing, 151–84. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42211-4_9.

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Wang, Xiangrong, Xianghua Wang, Weitong Zhai, and Kaiquan Cai. "Array Processing Fundamentals." In Sparse Sensing and Sparsity Sensed in Multi-sensor Array Applications, 3–21. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9558-5_1.

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Conference papers on the topic "Array processing"

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Baggeroer, A. B. "Sonar Arrays and Array Processing." In REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION. AIP, 2005. http://dx.doi.org/10.1063/1.1916655.

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Lombardo, P., R. Cardinali, D. Pastina, M. Bucciarelli, and A. Farina. "Array optimization and adaptive processing for sub-array based thinned arrays." In 2008 International Conference on Radar (Radar 2008). IEEE, 2008. http://dx.doi.org/10.1109/radar.2008.4653917.

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Wage, Kathleen E. "Multitaper Array Processing." In 2007 41st Asilomar conference on Signals, Systems and Computers (ACSSC). IEEE, 2007. http://dx.doi.org/10.1109/acssc.2007.4487424.

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GRIFFITHS, JWR. "SENSOR ARRAY PROCESSING." In Acoustics '91. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/21026.

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Kai-Bor Yu and M. F. Fernández. "Analog beamspace super-resolution radar processing." In 2010 IEEE International Symposium on Phased Array Systems and Technology (ARRAY 2010). IEEE, 2010. http://dx.doi.org/10.1109/array.2010.5613385.

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Yang, Shiwen, Gang Li, and Zaiping Nie. "Array signal processing in Four-Dimensional antenna arrays." In 2010 International Conference on Communications, Circuits and Systems (ICCCAS). IEEE, 2010. http://dx.doi.org/10.1109/icccas.2010.5581883.

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Mosher, Marianne, Michael Watts, Srba Jovic, Stephen Jaeger, Marianne Mosher, Michael Watts, Srba Jovic, and Stephen Jaeger. "Calibration of microphone arrays for phased array processing." In 3rd AIAA/CEAS Aeroacoustics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1997. http://dx.doi.org/10.2514/6.1997-1678.

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Doron, E., and M. A. Doron. "Coherent wideband array processing." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226011.

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Parker, Michael. "Adaptive Array Processing Architecture." In NAECON 2019 - IEEE National Aerospace and Electronics Conference. IEEE, 2019. http://dx.doi.org/10.1109/naecon46414.2019.9058177.

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Holmes, Neville. "Simplifying array processing languages." In the APL98 conference. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/327559.327621.

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Reports on the topic "Array processing"

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Kailath, Thomas. Sensor Array Processing. Fort Belvoir, VA: Defense Technical Information Center, February 1992. http://dx.doi.org/10.21236/ada262820.

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Himed, Braham. Conformal Array Adaptive Processing. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada446076.

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Nuttall, Albert H., and Andrew J. Knight. Model-Based Array Processing. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada633387.

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Steinhardt, Allan O. Hyperbolic Transforms in Array Processing. Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada247061.

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Friedlander, Benjamin. Array Processing for Discrete and Distributed Sources. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada428940.

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Edelblute, David J. Array Processing for a Multiple-Rank Signal. Fort Belvoir, VA: Defense Technical Information Center, May 1988. http://dx.doi.org/10.21236/ada197954.

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Oppenehim, Alan, and Arthur Baggeroer. Adaptive Array Processing in Uncertain Inhomogeneous Media. Fort Belvoir, VA: Defense Technical Information Center, August 1992. http://dx.doi.org/10.21236/ada254418.

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Amin, Moeness G. The 10th IEEE Signal Processing Workshop on Statistical Signal and Array Processing. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada383267.

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Buckley, Kevin, Richard Perry, and Joseph Teti. Space-Time Array Processing for Radar and Communications. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada427560.

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Hodgkiss, W. S. Shallow Water Adaptive Array Processing and Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada306525.

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