基于压缩感知的冲击波超压场重建方法

Reconstruction of shock wave overpressure field based on compressed sensing

在实际爆炸试验中,由于观测点数据稀疏,重建区域大等因素造成了数据量不足,利用传统迭代重建算法来重建冲击波超压场有其局限性。为提升单投影角度不完全投影数据条件下的成像效果,提出了一种基于TV最小化联合字典学习的冲击波超压场重建方法。结合压缩感知在稀疏约束方面的优势,利用TV正则化方法优化冲击波超压场的边缘信息,通过字典学习方法提高超压场内部细节信息的刻画程度,能够用较少的数据来重建冲击波超压场。经试验验证,与SART重建算法相比,TV-DL方法的重建精度有明显提升,其RMSE值降低了近40 m/s,且在每个网格内的相对误差减少了2.5%左右,实现了一种更高效的重建方法,在武器弹药毁伤评估、工程防护等领域,具有一定的理论意义和工程应用价值。

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 40 m/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.