Results 141 to 150 of about 270,830 (185)

DOA Estimation for Sparse Array via Sparse Signal Reconstruction

IEEE Transactions on Aerospace and Electronic Systems, 2013
The problem of direction-of-arrival (DOA) estimation for sparse array is addressed. The perspective that DOA estimation in virtual array response model can be cast as the problem of sparse recovery is introduced. Two methods are proposed, based on different optimization problems, which are solvable using second-order cone (SOC) programming. Without the
Zhongfu Ye, Ming Bao
exaly   +2 more sources

Coherent array imaging with sparse arrays

Proceedings., International Conference on Image Processing, 2002
For coherent, phased-array imaging systems, sparse arrays are often used in which the aperture is not completely filled by the array. Often, the data that are not collected by the array are set to zero and an image is obtained through a straight-forward Fourier inversion.
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Sonar array signal processing for sparse linear arrays

ISSPA '99. Proceedings of the Fifth International Symposium on Signal Processing and its Applications (IEEE Cat. No.99EX359), 2003
Acoustic signals which propagate through the ocean have wavefronts which can differ significantly from the "planar wavefronts" assumed in array signal processing. In this paper we investigate the performance of the the minimum variance distortionless response (MVDR) beamformer and the previously introduced Fourier integral method (FIM), when applied to
I. S. D. Solomon, A. J. Knight, M. V. Greening   +2 more
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Stochastic optimization of linear sparse arrays

IEEE Journal of Oceanic Engineering, 1999
In conventional beamforming systems, the use of aperiodic arrays is a powerful way to obtain high resolution employing few elements and avoiding the presence of grating lobes. The optimized design of such arrays is a required task in order to control the side-lobe level and distribution.
TRUCCO, ANDREA, V. MURINO
openaire   +3 more sources

Tensor MUSIC in multidimensional sparse arrays

2015 49th Asilomar Conference on Signals, Systems and Computers, 2015
Tensor-based MUSIC algorithms have been successfully applied to parameter estimation in array processing. In this paper, we apply these for sparse arrays, such as nested arrays and coprime arrays, which are known to boost the degrees of freedom to O(N2) given O(N) sensors.
Chun-Lin Liu, P. P. Vaidyanathan
openaire   +2 more sources

Difference bases and sparse sensor arrays

IEEE Transactions on Information Theory, 1993
Difference bases are discussed and their relevance to sensor arrays is described. Several new analytical difference base structures that result in near optimal low-redundancy sensor arrays are introduced. Algorithms are also presented for efficiently obtaining sparse sensor arrays and/or difference bases.
Darel A. Linebarger, Ivan Hal Sudborough, Ioannis G. Tollis   +2 more
openaire   +1 more source

Sparse-TPU

Proceedings of the 34th ACM International Conference on Supercomputing, 2020
While systolic arrays are widely used for dense-matrix operations, they are seldom used for sparse-matrix operations. In this paper, we show how a systolic array of Multiply-and-Accumulate (MAC) units, similar to Google's Tensor Processing Unit (TPU), can be adapted to efficiently handle sparse matrices.
Xin He 0011, Subhankar Pal, Aporva Amarnath, Siying Feng, Dong-Hyeon Park, Austin Rovinski, Haojie Ye, Kuan-Yu Chen 0001, Ronald G. Dreslinski, Trevor N. Mudge   +9 more
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A Beamformer Based on Sparse Array with Various Array Structures

2018
The degrees of freedom (DOF) of the adaptive beamformer based on conventional linear array is limited by the number of physical array sensors. One way to enhance the DOF is to choose sparse array, whose DOF is greater than the number of sensors in the array.
Yanqi Fan, Lei Yu 0011, Yinsheng Wei, Rongqing Xu   +3 more
openaire   +1 more source

Sparse Bayesian learning for beamforming using sparse linear arrays

The Journal of the Acoustical Society of America, 2018
Sparse linear arrays such as co-prime and nested arrays can resolve more sources than the number of sensors. In contrast, uniform linear arrays (ULA) cannot resolve more sources than the number of sensors. This paper demonstrates this using Sparse Bayesian learning (SBL) and co-array MUSIC for single frequency beamforming.
Santosh, Nannuru, Ali, Koochakzadeh, Kay L, Gemba, Piya, Pal, Peter, Gerstoft   +4 more
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Low Sidelobe Sparse Array Processing

Digital Signal Processing, 2002
Abstract Foster, S., Low Sidelobe Sparse Array Processing, Digital Signal Processing 12 (2002) 360–371 The coherent multiweight beamformer (CMWB) recently proposed by the author is analyzed from the point of view of its co-array structure. It is shown that for certain classes of sparse arrays it is possible to design CMWB beamformers to an ...
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