说明：  为提高压缩感知重构精度，该文提出一种分段弱阈值修正共轭梯度追踪算法。该算法修正了方向追踪算法 的方向，明确给出了搜寻原子下标的停止迭代准则，利用搜寻所得下标集通过最小二乘法得到稀疏信号的估计值。 仿真结果表明在同等稀疏的条件下实现精确重构，该算法与匹配追踪(MP)算法和分段正交匹配追踪FDR 阈值算 法(StOMP-FDR)相比,所需的观测值个数少20 ；在处理2 维图像信号时，其重构精度比分段正交匹配追踪FAR 阈值算法(StOMP-FAR)和贝叶斯算法(BCS)高1 。
(In order to improve recovery accuracy for compressed sensing, a Stagewise Weak selection Modifying approximation Conjugate Gradient Pursuit (StWMCGP) algorithm is proposed in this paper. This algorithm modifies the direction in the directional pursuit algorithm and clearly presents a stopping criterion to search the indices of elements and get a set. Then the evaluation of sparse signal is obtained by using Least-squares algorithm and the set. Simulated results show that for the same sparsity level, the number of measurements needed by the algorithm is about 20 less than that needed by MP or StOMP-FDR to exactly recover. When recovering two-dimensional image signal, the recovery accuracy of this algorithm is about 1 higher than that of BCS or StOMP-FAR.)