块稀疏贝叶斯算法因其良好的重建性能被广泛用于探地雷达研究中,但是传统块稀疏贝叶斯算法的提出针对于实数信号,它不能直接用于复数信号的重构。因此,提出一种复块稀疏贝叶斯压缩感知成像算法。此算法通过建立稀疏贝叶斯模型和应用复高斯尺度混合模型完成对目标反射系数的重构,将块稀疏贝叶斯学习模型从实数领域拓展至复数领域,并且使用GPRMAX仿真软件建立探地雷达的情景,获得时域数据,重构出地下目标的位置信息。实验结果表明:相比于其他算法,所提算法在低信噪比下成像效果更好。

Block sparse Bayesian learning algorithm is widely used in ground penetrating radar (GPR) because of its good reconstruction performance. However, classical block sparse Bayesian algorithm is proposed for real signals and cannot directly reconstruct complex signals. Therefore, a complex block sparse Bayesian compressed sensing imaging algorithm is proposed. In this algorithm, the reflection coefficient of the target is reconstructed by establishing the sparse Bayesian model and applying the complex Gaussian scale hybrid model, which extends the block sparse Bayesian learning model from the real field to the complex field. In addition, the GPRMAX simulation software is used to establish the scene of GPR, obtain the time domain data, and reconstruct the location information of the underground target. The experimental results show that the proposed algorithm performs better at low signal-to-noise ratio than other algorithms.

TN957.51

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