Compressive sensing (CS) is a new sampling theory used in many signal processing applications due to its simplicity and efficiency. However, signal reconstruction is considered as one of the biggest challenge faced by the CS method. Therefore in this paper, we aim to address this challenge by proposing an Adaptive Iterative Forward–Backward Greedy Algorithm (AFB). AFB algorithm is different from all other reconstruction algorithms, as it depends on solving the least squares problem in the forward phase, which increases the probability of selecting the correct columns better than other reconstruction algorithms. In addition, AFB improves the selection process by removing the incorrect columns selected in the previous step. To evaluate the AFB’s reconstruction performance, we used two types of data: computer-generated data and real data set (Intel Berkeley data set). The simulation results show that AFB outperforms Forward–Backward Pursuit, Subspace Pursuit, Orthogonal Matching Pursuit, and Regularized OMP in terms of reducing reconstruction error.

压缩感测（CS）由于其简单性和效率，是一种在许多信号处理应用中使用的新采样理论。但是，信号重建被认为是CS方法面临的最大挑战之一。因此，在本文中，我们旨在通过提出一种自适应迭代前向后向贪婪算法（AFB）来应对这一挑战。AFB算法不同于所有其他重建算法，因为它依赖于解决前向阶段的最小二乘问题，与其他重建算法相比，它能更好地选择正确的列。此外，AFB通过删除上一步中选择的不正确的列来改善选择过程。为了评估空军基地的重建性能，我们使用了两种类型的数据：计算机生成的数据和真实数据集（Intel Berkeley数据集）。仿真结果表明，在减少重构误差方面，AFB的性能优于前向后向追踪，子空间追踪，正交匹配追踪和正则化OMP。