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research article

Generating Sparse Stochastic Processes Using Matched Splines

Dadi, Leello  
•
Aziznejad, Shayan  
•
Unser, Michael  
January 1, 2020
Ieee Transactions On Signal Processing

We provide an algorithm to generate trajectories of sparse stochastic processes that are solutions of linear ordinary differential equations driven by Levy white noises. A recent paper showed that these processes are limits in law of generalized compound-Poisson processes. Based on this result, we derive an off-the-grid algorithm that generates arbitrarily close approximations of the target process. Our method relies on a B-spline representation of generalized compound-Poisson processes. We illustrate numerically the validity of our approach.

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Type
research article
DOI
10.1109/TSP.2020.3011632
Web of Science ID

WOS:000562044500009

Author(s)
Dadi, Leello  
Aziznejad, Shayan  
Unser, Michael  
Date Issued

2020-01-01

Published in
Ieee Transactions On Signal Processing
Volume

68

Start page

4397

End page

4406

Subjects

Engineering, Electrical & Electronic

•

Engineering

•

stochastic processes

•

white noise

•

random variables

•

technological innovation

•

signal processing algorithms

•

splines (mathematics)

•

differential equations

•

sparse stochastic processes

•

levy driven carma processes

•

b-splines

•

compound-poisson processes

•

cardinal exponential splines

•

unified formulation

•

part ii

•

simulation

•

driven

•

motion

•

noise

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

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LIB  
LIONS  
Available on Infoscience
September 9, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/171486
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