We propose a unified dynamic tracking algorithmic framework (PLAY-CS) to reconstruct signal sequences with their intrinsic structured dynamic sparsity. By capitalizing on specific statistical assumptions concerning the dynamic filter of the signal sequences, the proposed framework exhibits versatility by encompassing various existing dynamic compressive sensing (DCS) algorithms. This is achieved through the incorporation of a newly proposed Partial-Laplacian filtering sparsity model, tailored to capture a more sophisticated dynamic sparsity. In practical scenarios such as dynamic channel tracking in wireless communications, the framework demonstrates enhanced performance compared to existing DCS algorithms.

翻译：我们提出了一种统一的动态追踪算法框架（PLAY-CS），用于重构具有内在结构化动态稀疏性的信号序列。通过利用关于信号序列动态滤波器的特定统计假设，所提框架通过涵盖多种现有动态压缩感知（DCS）算法展现出通用性。这一目标的实现得益于一种新提出的部分拉普拉斯滤波稀疏性模型，该模型旨在捕捉更为复杂的动态稀疏性。在无线通信中的动态信道追踪等实际场景中，该框架相比现有DCS算法表现出更优的性能。