A Multi-task Dynamic Compressed Sensing Algorithm for Streaming Signals Eliminating Blocking Effects

The performance of Multi-task compressed sensing for streaming signals is restricted by blocking effects caused by block sparse transformation. To solve this problem, a multi-task dynamic compressed sensing algorithm based on sparse Bayesian learning is proposed in this paper, which combines multi-task compressed sensing with sliding window based on LOT transform. Experiments show that the proposed algorithm has higher reconstruction accuracy and operation efficiency compared with its block DCT based version.