[PDF] Top 20 A unified approach to sparse signal processing
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A unified approach to sparse signal processing
... of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully ...and processing manipulations through ... See full document
45
On the Primal Dual Method of Multipliers and its Applications
... two signal processing applications for these ...tributed sparse near-field acoustic beamformer and a distributed image fusion approach, for use in wireless sensor networks, and demonstrate how ... See full document
184
Multi linear sparse reconstruction for SAR imaging based on higher order SVD
... in signal processing. In 2010, Lim and Comon proposed a sparse reconstruc- tion method based on low-rank tensor decomposition and analyzed the condition of uniqueness for decomposition ...tensor ... See full document
11
Sparse Array Signal Processing: New Array Geometries, Parameter Estimation, and Theoretical Analysis
... new approach to sensor array processing because of the high degrees of freedom offered in the difference-coarray ...of sparse arrays obtained from a union of two uniform linear arrays (ULAs) with ... See full document
305
On a unified framework for linear nuisance parameters
... (OSP) approach projects out the nuisance term u such that the resulting observation vector is only sub- ject to x ...differential signal processing approach firstly chooses a reference and ... See full document
14
Unified performance measures in network localization
... DOA estimation, the presented framework is applicable in general. Hence, it may be applied to cooperative ranging or time-difference-of-arrival (TDOA)-based localization systems. RSS-based DOA is a promising ... See full document
17
Sparse Deconvolution Using Support Vector Machines
... Our approach follows two consecutive ...linear signal processing framework presented in [28] to create a robust, linear SVM algorithm for ...This approach, called primal signal model ... See full document
13
Construction of Sparse Representations of Perfect Polyphase Sequences in Zak Space with Applications to Radar and Communications
... chirp signal processing have been proposed ...alternative approach based on the finite Zak transform ...space approach is advantageous for several ...“window” signal, which often ... See full document
14
Sparse signal processing on estimation grid with constant information distance applied in radar
... the approach from ...the signal amplitude a be a function of delay, i.e., a → a(τ). The signal amplitude a (that is independent of τ ) is our refer- ence ...received signal is modeled in ... See full document
9
A novel aliasing free subband information fusion approach for wideband sparse spectral estimation
... array processing is to decompose the wideband signals into many narrowband signals with a filter bank or the discrete Fourier transform (DFT), and two categories, referred to as incoherent signal subspace ... See full document
13
Improved iterative shrinkage thresholding for sparse signal recovery via Laplace mixtures models
... Figures 6, 7, 8, and 9 (right) show the average run- ning times (CPU times in seconds) of the algorithms computed over the successful experiments, and the error bar represents the standard deviation of uncertainty for ... See full document
26
Sparse and smooth canonical correlation analysis through rank 1 matrix approximation
... for sparse and smooth ...this approach, the sparse and smooth CCA algorithms proposed differ only in the penalty used in the penalized rank-1 matrix approxima- ...proposed sparse CCA ... See full document
14
A clean signal reconstruction approach for coherently combining multiple radars
... estimation approach based on clean signal reconstruction is ...this approach is to reconstruct the clean sig- nal that is not or rarely interfered by cross-correlation energy ...posed approach ... See full document
11
Exploiting sensor mobility and covariance sparsity for distributed tracking of multiple sparse targets
... ing sparse regression and statistical inference, ...into sparse factors. The sparse matrix factorization techniques in [23, 24] are integrated here with proper sensor kinematic strategies and ... See full document
15
Discrete Fourier Transform: Approach To Signal Processing
... length signal. DFT can convert time‐domain discrete signal into frequency domain discrete ...for signal x [n], where n vary from n = 0 to N – 1. Then DFT of the signal is a sequence for ... See full document
8
Sparse signal recovery with unknown signal sparsity
... the sparse solution of h, i.e., recover the sparse signal, one can adopt either the convex relaxation based method, ...the signal sparsity k must be specified so that the computation stops ... See full document
8
Error bounds of block sparse signal recovery based on q ratio block constrained minimal singular values
... block sparse signal recovery in compressive sensing: (i) we establish a sufficient condition based on the q-ratio block sparsity for the exact recovery from the noise-free block BP (BBP) and develop a ... See full document
12
Byzantine Music Intervals: An Experimental Signal Processing Approach
... and processing the piece in order to reflect the best audio quality ...the signal, but also the format the producer wants to save and use to distribute the musical piece has an effect on the ... See full document
97
Unified LMI based design of ΔΣmodulators
... information signal. The frequency notch for the input signal appears at 100 Hz, which is the same with the sinusoidal wave, and the magnitude of quantization noise is low in the ... See full document
14
Clustering K SVD for sparse representation of images
... of sparse coefficients, and different number of iterative cycles in this part of ...the sparse representation of test ...peak signal to noise ratio (PSNR), and the results are presented in ... See full document
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