In this paper, we address the problem of tracking time-varying support of a sparse signal given a sequence of observation vectors. We model the dynamic variation of the support set using the discrete-state Markov process and employ the Rao-Blackwellized sequential Monte Carlo method, which allows for separate tracking of the support set and the amplitude of the unknown signals. Specifically, the samples for the support variables are drawn from their posteriori joint distributions using a Gibbs sampler while the continuous amplitude variables are separately estimated using the Kalman filter. Our numerical evaluation shows that the proposed method achieves significant performance gain over the existing sparse estimation methods.

中文翻译：

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通过序列蒙特卡罗方法估计稀疏信号的动态变化支持

在本文中，我们解决了在给定观察向量序列的情况下跟踪稀疏信号的时变支持的问题。我们使用离散状态马尔可夫过程对支持集的动态变化进行建模，并采用 Rao-Blackwellized 顺序蒙特卡罗方法，该方法允许单独跟踪支持集和未知信号的幅度。具体来说，支持变量的样本是使用吉布斯采样器从它们的后验联合分布中抽取的，而连续幅度变量则是使用卡尔曼滤波器单独估计的。我们的数值评估表明，与现有的稀疏估计方法相比，所提出的方法取得了显着的性能提升。