Predictive algorithms in dynamical sampling for burst-like forcing terms
Predictive algorithms in dynamical sampling for burst-like forcing terms
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DOI：
10.1016/j.acha.2023.03.003
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
A. Aldroubi;Longxiu Huang;K. Kornelson;I. Krishtal
中科院分区：
文献类型：
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作者：
A. Aldroubi;Longxiu Huang;K. Kornelson;I. Krishtal
在动态采样的框架下，研究了初值问题（IVP）中类突发强迫项的恢复问题。我们介绍了使用两种特殊类型的采样器的想法，这两种采样器允许人们在没有突发的时间间隔内预测IVP的解。这导致了两种不同的算法，即使在存在测量采集误差和大背景源的情况下，也能稳定而准确地近似爆发强迫项。
In this paper, we consider the problem of recovery of a burst-like forcing term in an initial value problem (IVP) in the framework of dynamical sampling. We introduce an idea of using two particular classes of samplers that allow one to predict the solution of the IVP over a time interval without a burst. This leads to two different algorithms that stably and accurately approximate the burst-like forcing term even in the presence of a measurement acquisition error and a large background source.