High Resolution Estimation of Directions of Arrival

Karabulut, G.Z.; Kurt, Tolga; Yongaçoğlu, Abbas

doi:10.1109/vetecs.2005.1543241

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…DOA estimation [19][20][21] on a PD ultrasonic array signals using an MP algorithm is a sparse decomposition process on signal X (t) received by an array. By getting the angle of the non-zero vector atom selected from over complete dictionary of atoms A S (θ) according to the decomposition results, the DOA estimation angle can be obtained.…”

Section: 3 the Mp-based Doa Estimation Algorithm For A Pd Ultrasonimentioning

confidence: 99%

Application of signal sparse decomposition in the detection of partial discharge by ultrasonic array method

Xie

Wang

et al. 2015

IEEE Trans. Dielect. Electr. Insul.

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The detection of partial discharge of power transformer is important for the safe operation of a power system. The direction of arrival is an important part of ultrasonic array detection. Classic DOA estimation algorithms based on subspace analysis do not perform well under the conditions of low SNR and small snapshot numbers. The problem with using an improved algorithm is that it requires a huge amount of calculation. In this paper sparse signal decomposition is applied to spatial spectrum estimation. The authors derived the model of array signal direction finding by the use of matching pursuit algorithm, setting up overcomplete dictionaries for different types of sensor array and simulating different types of sensor array receiving signals. Simulations and experiments show that direction finding based on MP decomposition method performs better than using the MUSIC algorithm under the conditions of low SNR and small snapshot numbers, and that the influence of array types is less.Index Terms -Partial discharge ultrasonic array detection, sparse signal decomposition, direction of arrival, overcomplete vector dictionary.

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Section: 3 the Mp-based Doa Estimation Algorithm For A Pd Ultrasonimentioning

confidence: 99%

Application of signal sparse decomposition in the detection of partial discharge by ultrasonic array method

Xie

Wang

et al. 2015

IEEE Trans. Dielect. Electr. Insul.

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“…Compressive sensing is a term for a number of methods to solve for sparse solutions to underdetermined linear problems. Compressive sensing has been considered for angular superresolution in radar array processing in [12], [14], [15], [22]. In [15] and [1], it was shown that compressive sensing is equivalent to MAP estimation with a Laplacian prior on the complex amplitudes.…”

Section: Compressive Sensing Estimationmentioning

confidence: 99%

“…Due to the brute force nature of the solution, it is not feasible when a large number of targets is present, though it can be easily parallelized across many processors. Indeed, with a moderate number of targets and good parallelization, the brute-force approach could be faster than algorithms such as [12], which is hard to parallelize, since it requires a large amount of branching to search a tree structure. This brute force approach shall be considered as a "best case" solution to the compressive sensing problem, as typically, approximate solutions are used.…”

Section: Compressive Sensing Estimationmentioning

confidence: 99%

CS versus MAP and MMOSPA for multi-target radar AOAs

Crouse

Willett

Bar‐Shalom

et al. 2011

2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

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We expand upon existing the literature regarding using Minimum Mean Optimal Sub-Pattern Assignment error (MMOSPA) estimates in multitarget tracking to apply it to angular superresolution of closely-space targets, noting its advantages in comparison to Maximum a Posteriori (MAP) and Minimum Mean Squared Error (MMSE) estimation. MMOSPA estimators sacrifice target labeling, but in doing so they can (often) avoid coalescence of estimates of closely-spaced objects. A compressive sensing solution, which is a form of MAP estimation, is also considered and is solved via a brute force search, which, contrary to popular belief, is computationally feasible when the number of targets is low, having execution times on the order of tens of milliseconds for two targets on a linear array. 8 We shall note the identities for complex vectors that ∂ ∂b * a H b = 0 and ∂ ∂b * b H a = a. The gradient vector of a complex function, J, is simply ∇ b J = 2 ∂ ∂b * J. 9 Note that A H Q −1 A is real and that (XY) H = Y H X H .

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