Sparse Signal Processing

Azghani, Masoumeh; Marvasti, Farokh

doi:10.1007/978-3-319-08801-3_8

Cited by 22 publications

(6 citation statements)

References 29 publications

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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%

Dynamic Compensation of Piezoresistive Pressure Sensor Based on Sparse Domain

Han

Liu

et al. 2020

Journal of Sensors

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In the process of transient test, due to the insufficient bandwidth of the pressure sensor, the test data is inaccurate. Firstly, based on the projection of the shock tube test signal in the sparse domain, the feature expression of the signal sample is obtained. Secondly, the problem of insufficient bandwidth is solved by inverse modeling of sensor dynamic compensation system based on swarm intelligence algorithm. In this paper, the method is used to compensate the shock tube test signals of the 85XX series pressure sensors made by the Endevco company of the United States, the working bandwidth of the sensor is widened obviously, the rise time of the pressure signal can be compensated to 12.5 μs, and the overshoot can be reduced to 8.96%. The repeatability of dynamic compensation is verified for the actual gun muzzle shock wave test data, the results show that the dynamic compensation can effectively recover the important indexes such as overpressure peak value and positive pressure action time, and the original shock wave signal is recovered from the high resonance data.

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Section: Design Of Dynamic Compensation Filtermentioning

confidence: 99%

Dynamic Compensation of Piezoresistive Pressure Sensor Based on Sparse Domain

Han

Liu

et al. 2020

Journal of Sensors

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“…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%

2D off‐grid DOA estimation using joint sparsity

Afkhaminia¹,

Azghani²

2019

IET Radar, Sonar & Navigation

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“…||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%

Iterative null space projection method with adaptive thresholding in sparse signal recovery

Esmaeili

Kangarshahi

Marvasti

2018

IET signal process.

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Adaptive thresholding methods have proved to yield high Signal-to-Noise Ratio (SNR) and fast convergence for sparse signal recovery. Recently, it was observed that the robustness of a class of iterative sparse recovery algorithms such as Iterative Method with Adaptive Thresholding (IMAT) outperforms the well-known LASSO algorithm in terms of reconstruction quality, convergence speed, and the sensitivity to the noise. In this paper, we introduce a new method towards sparse signal recovery from random samples (RS) of the sensing matrix or its generalized version Compressed Sensing (CS) problem. The logic of this method is based on iterative projections of the thresholded signal onto the null-space of the sensing matrix. The simulations results reveal that the proposed method has the capability of yielding noticeable output SNR values when the number of samples approaches twice the sparsity number, while other methods fail to recover the signals when approaching this number. We have also extended our algorithm to Matrix Completion (MC) scenarios and compared its efficiency to other well-known approaches for MC in the literature.

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Sparse Signal Processing

Cited by 22 publications

References 29 publications

Dynamic Compensation of Piezoresistive Pressure Sensor Based on Sparse Domain

Dynamic Compensation of Piezoresistive Pressure Sensor Based on Sparse Domain

2D off‐grid DOA estimation using joint sparsity

Iterative null space projection method with adaptive thresholding in sparse signal recovery

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