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Sum-of-exponentials modeling and common dynamics estimation using Tensorlab

机译:使用Tensorlab进行指数总和建模和共同动力学估计

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Fitting a signal to a sum-of-exponentials model is a basic problem in signal processing. It can be posed and solved as a Hankel structured low-rank matrix approximation problem. Subsequently, local optimization, subspace, and convex relaxation methods can be used for the numerical solution. In this paper, we show another approach, based on the recently developed concept of structured data fusion. Structured data fusion problems are solved in the Tensorlab toolbox by local optimization methods. The approach allows fitting of signals with missing samples and adding constraints on the model, such as fixed exponents and common dynamics in multi-channel estimation problems. These problems are non-trivial to solve by other existing methods. Tensorlab is publicly available and the results presented are reproducible.
机译:使信号适合指数和模型是信号处理中的一个基本问题。它可以作为Hankel结构的低秩矩阵逼近问题提出和解决。随后,可以将局部优化,子空间和凸松弛方法用于数值解。在本文中,我们展示了基于最近开发的结构化数据融合概念的另一种方法。通过局部优化方法在Tensorlab工具箱中解决了结构化数据融合问题。该方法允许对缺少样本的信号进行拟合,并在模型上增加约束,例如固定指数和多通道估计问题中的常见动态。通过其他现有方法解决这些问题并非易事。 Tensorlab是公开可用的,给出的结果是可重现的。

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