Based on compressed sensing technology, a DOA estimation algorithm for reconstructing the original signal is proposed. First, the over-complete dictionary of signal vectors is established based on the sparsity of sign...
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Based on compressed sensing technology, a DOA estimation algorithm for reconstructing the original signal is proposed. First, the over-complete dictionary of signal vectors is established based on the sparsity of signals in spatial domain. The DOA estimation problem is transformed into sparse signals reconstruction problem, and signals in spatial domain are reconstructed using iterativesoftthresholding (IST) algorithm. At the same time, combined with the second-order statistical characteristic of non-circular signal, the array is virtual extended, and the spectrum estimation is performed by MUSIC algorithm. In addition, in order to further distinguish coherent signals, spatial smoothing technology is added to the algorithm. The simulation results show that the proposed algorithm can effectively resolve incoherent signals and coherent signals under small snapshots and low SNR.
The iterative soft thresholding algorithm (ISTA) is one of the most popular optimization algorithms for solving the l(1) regularized least squares problem, and its linear convergence has been investigated under the as...
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The iterative soft thresholding algorithm (ISTA) is one of the most popular optimization algorithms for solving the l(1) regularized least squares problem, and its linear convergence has been investigated under the assumption of finite basis injectivity property or strict sparsity pattern. In this paper, we consider the l(1) regularized least squares problem in finite-or infinite-dimensional Hilbert space, introduce a weaker notion of orthogonal sparsity pattern (OSP) and establish the Q-linear convergence of ISTA under the assumption of OSP. Examples are provided to illustrate the cases where the linear convergence of ISTA can be established only by our result, but cannot be ensured by any existing result in the literature.
The deconvolution method (DM) is an effective tool for enhancing the impulsive features of rolling bearings. Deep network-based deconvolution methods transform complex numerical computations into network optimization,...
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The combination of SAR (synthetic aperture radar) and FMCW (frequency mod- ulation continuous wave) technology offers a high-accuracy, cost-effective imaging sensor for remote sensing. Instead of treating the target a...
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The combination of SAR (synthetic aperture radar) and FMCW (frequency mod- ulation continuous wave) technology offers a high-accuracy, cost-effective imaging sensor for remote sensing. Instead of treating the target as a continuous object, this technology sees the target as several scattering points with certain amplitude which depend on the reflecting intensity. In this thesis, we implement two image reconstruction techniques: Gridding method (widely used in medical imaging) and unequally spaced FFTs (has advantages for arbitrary sampling geometries) to set up the scattering centers model. Then taking account of efficiency and saving time for further data process in self-driving area, only a few of the scattering centers having stronger amplitudes will be kept by doing sparse representation technology. Here, we choose the iterative soft thresholding algorithm based on l 1 -norm minimization to fulfill sparse representation. We then discuss the trade-off between the choice of threshold and the number of iterations for the optimization problem to converge.
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