Sparse Signal Reconstruction: LASSO and Cardinality Approaches

Boyko, Nikita; Karamemis, Gulver; Kuzmenko, Viktor; Uryasev, Stan

doi:10.1007/978-3-319-10046-3_4

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…In LASSO, with the assumptipn that x is sparse, the L1-norm regularization is incorporated into the objective function to supress non-contributive elements in x. The CS problem is frst solved with diferent regularization parameters, following which the solution with appropriate regression error and cardinality will be selected [16]. The major advantage of LASSO method is its computational efciency (a convex optimization) even for a very large signal reconstruction problem.…”

Section: Basics Of Cs Methodsmentioning

confidence: 99%

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Vehicle axle detection from under-sampled signal through compressed-sensing-based signal recovery

Zhang

Huang

Bridgelall

2022

J Civil Struct Health Monit

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In trafc data collection, sampling design should satisfy the requirements of identifying prominent pulses corresponding to vehicle axle passage. Insufcient measurement leads to signal distortion and attenuation, reducing the quality of signal pulses. This study exploits the value of under-sampled data by applying compressed sensing (CS) methods to recover signal components that are critical for vehicle axle detection. Two CS methods are investigated in this study to recover the strain signal pulses from inside-pavement instrumented sensors at high-speed traversals. The CS methods successfully recovered the signal pulses from all axles of the truck used for testing. A comparison of the measured axle distances with the reference measurements validated the efectiveness of signal recovery methods. Therefore, the CS methods have the potential of reducing the cost, energy consumption, and data storage space, and improving the data transmission efciency in practical implementations by enabling sampling devices designed for static measurements to achieve dynamic measurements.

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Section: Basics Of Cs Methodsmentioning

confidence: 99%

“…This paper obviates elaborating these two methods due to the limited length. The authors refer to [15] for details on the development of CoSaMP, and to [16] for details on the LASSO method.…”

Section: Signal Recovery For Axle Detection: Cs From Under-sampled Si...mentioning

confidence: 99%

Vehicle axle detection from under-sampled signal through compressed-sensing-based signal recovery

Zhang

Huang

Bridgelall

2022

J Civil Struct Health Monit

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“…the obtained approximation of the Prony polynomial has in most cases the minimal number of non zero coefficients in its monomial representation, see e.g. [4,5,24]. Thus, an l 1 -solution for the underdetermined analogues of (4) resp.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation and signal reconstruction

Skrzipek

2021

Calcolo

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In frequency analysis an often appearing problem is the reconstruction of a signal from given samples. Since the samples are usually noised, pure interpolating approaches are not recommended and appropriate approximation methods are more suitable as they can be interpreted as a kind of denoising. Two approaches are widely used. One uses the reflection coefficients of a finite sequence of Szegő polynomials and the other one the zeros of the so called Prony polynomial. We show that both approaches are closely related. As a kind of inverse problem, it’s not surprising that they have in common that both methods depend very sensitive on sampling errors. We use known properties of the signal to estimate the positions of the zeros of the corresponding Szegő- or Prony-like polynomials and construct adaptive algorithms to calculate these ones. Hereby, we get the corresponding parameters in the exponential parts of the signal, too. Then, the coefficients of the signal (as a linear combination of such exponential functions) can be obtained from a system of linear equations by minimizing the residuals with respect to a suitable norm as a kind of denoising.

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Synthesis of Waveform Covariance Matrix for MIMO Radar Transmit Beampatterns: LASSO and IRLS Approaches

Rihan

2018

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

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Sparse Signal Reconstruction: LASSO and Cardinality Approaches

Cited by 3 publications

References 14 publications

Vehicle axle detection from under-sampled signal through compressed-sensing-based signal recovery

Vehicle axle detection from under-sampled signal through compressed-sensing-based signal recovery

Parameter estimation and signal reconstruction

Synthesis of Waveform Covariance Matrix for MIMO Radar Transmit Beampatterns: LASSO and IRLS Approaches

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