Due to the sparse data and large reconstruction area in the actual explosion test, the amount of data is insufficient. The traditional iterative reconstruction algorithm has its limitations in the reconstruction of the shock wave overpressure field. In order to improve the imaging effect under the condition of incomplete projection data with single projection angle, a reconstruction method combining total variation minimization and dictionary learning is proposed in this paper. Combining the advantages of compressed sensing in sparse constraints, the TV regularization method is used to optimize the edge information of the shock wave overpressure field, and the dictionary learning method is used to improve the detail characteristics of the shock wave field, which can reconstruct the shock wave overpressure field with less data. The analysis shows that compared with the SART algorithm, the proposed method can significantly improve the reconstruction quality, its RMSE value is reduced by nearly 40m/s, and the relative error in each grid is reduced by about 2.5%, and a more efficient reconstruction method is realized. It has certain theoretical significance and engineering application value in weapon and ammunition damage assessment and engineering protection.