Sparse Bayesian signal reconstruction method based on multiple measurement vector models

The invention discloses a sparse Bayesian signal reconstruction method based on a multiple measurement vector model, which comprises the following steps of: S1, giving a base station receiving signal, an equivalent channel matrix, a device number and a spread spectrum gain, and distributing structured prior Gaussian information to a transmitting signal by combining an automatic decision-making technology, common Gaussian prior information is distributed to the noise signals, and a hyper-parameter set is initialized; s2, calculating posterior distribution of the transmitted signals by adopting the Bayesian theorem; s3, updating the hyper-parameter set through an iterative expectation maximization method; and S4, judging whether an iteration termination condition is met or not, if so, exiting the loop, and outputting a recovered transmission signal, and if not, returning to the step S2 to carry out next iteration. According to the method, internal structuring of user information is utilized, multi-user detectio