Performance of dynamic sparse recovery for streaming signals in time domain will degrade for the existence of blocking artifacts and unknown time-varying noise intensity. To solve the above problems

a robust sparse Bayesian learning algorithm for dynamic compressive sensing of streaming signals in time domain is proposed based on the framework of lapped orthogonal transform and sparse Bayesian learning. In addition to eliminating the blocking artifacts

the proposed algorithm handles dynamic sparse Bayesian learning problems effectively under conditions of unknown time-varying noise intensity

which has better robustness against existing sparse Bayesian learning algorithms for streaming signals. Though there are not many existing effective algorithms for compressed sensing of streaming signals

experiments show that the proposed algorithm has obviously larger reconstruction signal-to-noise ratio and higher success rates for reconstruction than existing recovery algorithms for streaming signals based on sparse Bayesian learning or L1-homotopy; also

the measurement number required for particular success rates is obviously less than that of the other two algorithms

the computation cost and running time is approximately the same with the existing sparse Bayesian learning algorithm.