2014
DOI: 10.1007/978-3-319-08801-3_8
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Sparse Signal Processing

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Cited by 22 publications
(6 citation statements)
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“…Given the short duration of transient signal in the time domain, that is, the low signal information density in the whole acquisition process, the sparse characteristic of the signal under a certain base can be determined. At present, the commonly used sparse bases are DCT, FFT, and DWT, etc [11]. To determine the final sparse region of the signal, several classical sparse bases are selected to transform the step signal and shock tube signal, respectively, and the sparsity after transformation is compared.…”
Section: Design Of Dynamic Compensation Filtermentioning
confidence: 99%
“…Given the short duration of transient signal in the time domain, that is, the low signal information density in the whole acquisition process, the sparse characteristic of the signal under a certain base can be determined. At present, the commonly used sparse bases are DCT, FFT, and DWT, etc [11]. To determine the final sparse region of the signal, several classical sparse bases are selected to transform the step signal and shock tube signal, respectively, and the sparsity after transformation is compared.…”
Section: Design Of Dynamic Compensation Filtermentioning
confidence: 99%
“…In the last few decades, a variety of algorithms have been developed for DOA estimation such as eigenstructure‐based methods like multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT) [3], beamforming, and maximum likelihood methods [4]. By the development of sparse signal processing [5–7], the sparsity‐based DOA estimation methods have been proposed which have special advantages such as higher accuracy and resolution, lower computational complexity and robustness against noise. A signal is called sparse when most of its entries are zero in some domain [5–7].…”
Section: Introductionmentioning
confidence: 99%
“…By the development of sparse signal processing [5–7], the sparsity‐based DOA estimation methods have been proposed which have special advantages such as higher accuracy and resolution, lower computational complexity and robustness against noise. A signal is called sparse when most of its entries are zero in some domain [5–7]. Sparse signal processing has found various applications ranging from medical imaging [8], video processing [9] to radar systems [10].…”
Section: Introductionmentioning
confidence: 99%
“…||y − Φx|| 2 2 ≤ . The solution to under-determined linear system is the sparsest solution if ||x|| 0 ≤ 1 1+µ(Φ) , where µ(Φ) is the coherence of the matrix Φ [9]. Knowing this, we have developed a new method which converges to the sparsest solution knowing the fact that if the solution is unique, it will be the sparsest one.…”
Section: Introductionmentioning
confidence: 99%