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首页> 外文会议>Information Sciences and Systems, 2009. CISS 2009 >Dynamic updating for sparse time varying signals
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Dynamic updating for sparse time varying signals

机译：动态更新稀疏时变信号

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摘要

Many signal processing applications revolve around finding a sparse solution to a (often underdetermined) system of linear equations. Recent results in compressive sensing (CS) have shown that when the signal we are trying to acquire is sparse and the measurements are incoherent, the signal can be reconstructed reliably from an incomplete set of measurements. However, the signal recovery is an involved process, usually requiring the solution of an lscr1 minimization program. In this paper we discuss the problem of estimating a time-varying sparse signal from a series of linear measurements. We propose an efficient way to dynamically update the solution to two types of lscr1 problems when the underlying signal changes. The proposed dynamic update scheme is based on homotopy continuation, which systematically breaks down the solution update into a small number of linear steps. The computational cost for each step is just a few matrix-vector multiplications.
机译：许多信号处理应用围绕寻找线性方程组（通常是不确定的）的稀疏解。压缩感测（CS）的最新结果表明，当我们尝试获取的信号稀疏且测量值不相干时，可以从一组不完整的测量值中可靠地重建信号。但是，信号恢复是一个复杂的过程，通常需要解决lscr 1 最小化程序的问题。在本文中，我们讨论了根据一系列线性测量来估计时变稀疏信号的问题。当基础信号发生变化时，我们提出了一种有效的方法来动态更新解决方案，以解决两种类型的lscr 1 问题。所提出的动态更新方案基于同伦连续性，该系统将解决方案更新系统性地分解为少量的线性步骤。每个步骤的计算成本只是几个矩阵向量乘法。

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