Results 1 to 10 of about 479,411 (163)
Direction of Arrival Estimation of Generalized Nested Array via Difference–Sum Co-Array [PDF]
Sensors, 2023 To address the weakness that the difference co-array (DCA) only enhances the degrees of freedom (DOFs) to a limited extent, a new configuration called the generalized nested array via difference–sum co-array (GNA-DSCA) is proposed for direction of ...
Yule Zhang +5 more
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Computationally Efficient Sources Location Method for Nested Array via Massive Virtual Difference Co-Array [PDF]
Sensors, 2019 In this paper, we derive the discrete Fourier transform (DFT) method for direction of arrival (DOA) estimation by generating the massive virtual difference co-array with the nested array. By contrast with the spatial smoothing (SS) subspace-based methods
Wei Wu +3 more
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Generalized L-Shaped Nested Array Concept Based on the Fourth-Order Difference Co-Array [PDF]
Sensors, 2018 In this paper, a generalized L-shaped nested array based on the fourth-order difference co-array is proposed for two-dimensional (2D) directions’ estimation.
Lei Zhang +4 more
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A Novel Nested Configuration Based on the Difference and Sum Co-Array Concept. [PDF]
Sensors (Basel), 2018 Recently, the concept of the difference and sum co-array (DSCa) has attracted much attention in array signal processing due to its high degree of freedom (DOF). In this paper, the DSCa of the nested array (NA) is analyzed and then an improved nested configuration known as the diff-sum nested array (DsNA) is proposed.
Chen Z, Ding Y, Ren S, Chen Z.
europepmc +5 more sources
Sparse Symmetric Linear Arrays With Low Redundancy and a Contiguous Sum Co-Array [PDF]
IEEE Transactions on Signal Processing, 2021 Sparse arrays can resolve significantly more scatterers or sources than sensor by utilizing the co-array - a virtual array structure consisting of pairwise differences or sums of sensor positions. Although several sparse array configurations have been developed for passive sensing applications, far fewer active array designs exist.
Robin Rajamäki, Visa Koivunen
exaly +5 more sources
A DOA Estimation Method for Sparse Array Based on DFT Spectrum of Received Signals
IEEE Access, 2022 In the direction of arrival (DOA) estimation of sparse array received signals, the estimation accuracy of the grid search method in compressed sensing is improved with the increase of over-complete redundant dictionary elements.
Liye Zhang +4 more
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On The Asymptotic Performance of One-Bit Co-Array-Based Music [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 Co-array-based Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable attention in array processing thanks to its capability of providing enhanced degrees of freedom for DoAs that can be resolved. Additionally, deployment of one-bit Analog-to-Digital Converters (ADCs) has become an important topic in ...
Saeid Sedighi +3 more
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Co-arrays in the next Fortran Standard [PDF]
ACM SIGPLAN Fortran Forum, 2005 The WG5 committee, at its meeting in Delft, May 2005, decided to include co-arrays in the next Fortran Standard. A special issue of Fortran Forum in August 1998 explained the feature, but since many of the details of the version adopted by WG5 differ from the 1998 version, it seems appropriate to describe it afresh in a self-contained article.A Fortran
John K. Reid, Robert W. Numrich
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IET Radar, Sonar & Navigation, 2023 Sparse arrays with O(N) sensors can offer ON2 number of degrees of freedom by exploiting the difference co‐array that can be obtained from the second‐order statistics of received signals. In this article, a novel sparse array with seven subarrays (SA‐7S)
Baohua Zhang +4 more
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Extending C++ with co-array semantics [PDF]
Proceedings of the 3rd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming, 2016 The current trend of large scientific computing problems is to align as much as possible to a Single Programming Multiple Data (or SPMD) scheme when the application algorithms are conducive to parallelization and vectorization. This reduces the complexity of code because the processors or (computational nodes) perform the same instructions which allows
Antoine Tran Tan, Hartmut Kaiser
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