  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.
