层析成像系统的自适应压缩重构

Adaptive Compression Reconstruction of Tomography System

针对多散射多传播路径的射频层析成像稀疏系统出现虚假目标影响图像重构的问题,提出一种基于子空间追踪的自适应稀疏度重构方法。先根据目标信号自身特点动态调节稀疏度的起始值和步长逼近真实稀疏度,再利用子空间追踪算法将多路径线性模型的衰减系数稀疏化处理,并在重构过程中依靠稀疏度估计值更新支撑集,重构目标图像。与其他重构算法相比,该方法有效减少虚假目标对图像清晰度的影响,实现稀疏度未知的层析图像清晰重构。仿真实验分析系统的重构匹配度和虚假目标出现概率,比较射频传感器在有无噪声下算法的重构性能。实验结果表明,该算法可准确估计稀疏度,较低运算量的重构高精度图像,在射频层析成像其他领域得到较好的应用。

The problem of image reconstruction in the sparse system of radio frequency tomography with multiple scattering path is presented, and a self-adaptive sparse reconstruction method based on subspace tracking is proposed. The initial value and step length of the sparse degree are dynamically adjusted according to the characteristics of the target signal. And the attenuation coefficient of multipath linear model is sparse by using the subspace tracking algorithm. In the process of reconstruction, the supporting set is updated by the sparse estimation to reconstruct the target image. Compared with the other reconstruction algorithms, this method can effectively reduce the influence of the ghost on image definition and realize the clear reconstruction of tomography with unknown sparse. The reconstruction of the system and the probability of the ghost are presented. The experimental results show that the proposed algorithm can accurately estimate sparsity and the high precision image with low calculation amount, which can be used in other fields of radio frequency imaging.