The invention discloses a low-complexity sparse Bayesian vector estimation method and system. The method comprises the following steps: S1, setting initial values of dictionary matrix posteriori distribution and additive white Gaussian noise probability distribution hyper-parameters in a sparse Bayesian vector system; S2, on the basis of a preset system model and the initial values of the hyper-parameters, acquiring estimated values of dictionary matrix posteriori distribution and additive white Gaussian noise probability distribution hyper-parameters in the system by using an edge likelihoodmaximization algorithm; and S3, estimating a sparse vector of the system based on the estimated value of the algorithm probability distribution hyper-parameter. According to the low-complexity sparseBayesian vector estimation method and the low-complexity sparse Bayesian vector estimation system, a posterior probability model of a sparse vector is constructed and a sparse vector omega estimationproblem can be converted into an estimation problem of hyper-parameters d and sigma 2, and then a sparse Bayesian vector estimation algorithm is adopted to solve a problem that matrix inversion is needed in a common sparse Bayesian estimation algorithm, so that the reliable estimation performance is obtained with relatively low complexity.
Low-complexity sparse Bayesian vector estimation method and system