基于变步长的正则化回溯自适应追踪算法

在压缩感知重构算法中，稀疏度未知及步长大小固定是影响算法精度及运行时间的因素.针对以上不足，本文提出一种基于变步长的正则化回溯自适应追踪算法.该算法首先通过原子匹配测试的方式获得信号的稀疏度估计；将正则化思想和子空间追踪算法的回溯思想相结合，实现原子的二次筛选并筛除不合适的原子；最后，利用变化的步长选择候选集中的原子，帮助完成信号的完整重构.通过仿真实验证明，本文提出的重构算法在重构速度和重构精度上均优于同类算法.

Regularized Backtracking Adaptive Pursuit Algorithm Based Variable Step-size

In the compressive sensing reconstruction algorithm,the unknown sparsity and the fixed step-size are the factors that affect the reconstruction accuracy and running time of the algorithm.In view of the above shortcomings,we propose a regularized backtracking adaptive pursuit algorithm based variable step-size.Firstly,the sparsity of the signal is obtained by the way of atomic matching test.Then we combine the regularization method with the subspace tracking algorithm to achieve the second screening and remove the atoms which are not appropriate.Finally,we use a variable step-size to select atoms in the candidate set so that we can complete the signal reconstruction.The simulation results show that the proposed algorithm is superior to other algorithms in speed and reconstruction accuracy.