The invention discloses a self-adaptive sparse foresight super-resolution imaging method based on an echo restoration strategy, which comprises the following steps: firstly, establishing a scanning
radar super-resolution model, then, based on a regularization theory, using an L1 norm as a constraint term to improve the
angular resolution of a sparse target, and then, using a multi-scale representation characteristic of
wavelet variation to separate signals and
noise in echoes, so as to realize the self-adaptive sparse foresight super-resolution imaging. The problem that an existing L1-IRN method is poor in performance under the low
signal-to-
noise ratio is solved while the
echo signal-to-
noise ratio is improved, a new optimization cost function is constructed based on preprocessed echoes, finally, a sparse
estimation problem is converted into a maximum posteriori
estimation problem based on the Bayesian theory, a self-adaptive iteration weight is obtained, and foresight super-resolution imaging is achieved. The echo repairing strategy of the method can weaken the influence of noise on super-resolution imaging, adaptive sparse foresight super-resolution imaging is realized under the condition of extremely low
signal-to-noise ratio, a high-quality imaging result is obtained, meanwhile, the efficiency of a foresight two-dimensional super-resolution
imaging algorithm is improved, and high robustness is achieved.