标题： 重加权 l_1 动态滤波用于时变稀疏信号估计 显示英文标题

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

摘要： 从不完整的观测中进行信号估计，随着在推理过程中可以利用的信号结构越多，估计效果越好。经典算法（例如，卡尔曼滤波）已经利用了时变信号中的强动态结构，而现代工作则常常专注于利用静态信号的低维结构（例如，在基下的稀疏性）。很少有算法尝试将静态和动态结构结合起来，以提高对时变稀疏信号（例如，视频）的估计。在本工作中，我们提出了一种加权 l_1 动态滤波方案，用于因果信号估计，该方案同时利用了稀疏性假设和动态结构。我们的算法利用了层次拉普拉斯尺度混合模型的研究成果，以创建一个动态概率模型。该算法在估计过程中以稳健且高效的方式结合了动态和稀疏性先验。我们使用合成数据和自然数据在模拟中展示了结果。 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.