标题： 时变稀疏信号估计的重加权l_1动态滤波 显示英文标题

标题： Re-Weighted l_1 Dynamic Filtering for Time-Varying Sparse Signal Estimation

摘要： 从不完全观测中进行信号估计的改进在于能够利用更多的信号结构来进行推断过程。经典算法（例如，卡尔曼滤波）已针对时变信号利用了强大的动态结构，而现代研究则通常侧重于利用低维信号结构（例如，基函数中的稀疏性）来处理静态信号。很少有算法尝试将静态和动态结构结合起来，以改善时变稀疏信号（例如视频）的估计。在这项工作中，我们提出了一种用于因果信号估计的重新加权l_1动态滤波方案，该方案利用了稀疏假设和动态结构。我们的算法借鉴了分层Laplacian尺度混合模型的工作，构建了一个动态概率模型。由此产生的算法在估计过程中稳健且高效地结合了动态和稀疏性先验。我们在仿真中使用合成数据和自然数据展示了结果。 显示英文摘要

摘要： Signal estimation from incomplete observations improves as more signal structure can be exploited in the inference process. Classic algorithms (e.g., Kalman filtering) have exploited strong dynamic structure for time-varying signals while modern work has often focused on exploiting low-dimensional signal structure (e.g., sparsity in a basis) for static signals. Few algorithms attempt to merge both static and dynamic structure to improve estimation for time-varying sparse signals (e.g., video). In this work we present a re-weighted l_1 dynamic filtering scheme for causal signal estimation that utilizes both sparsity assumptions and dynamic structure. Our algorithm leverages work on hierarchical Laplacian scale mixture models to create a dynamic probabilistic model. The resulting algorithm incorporates both dynamic and sparsity priors in the estimation procedure in a robust and efficient algorithm. We demonstrate the results in simulation using both synthetic and natural data.