Now-a-days due to advancements in technologies most of the applications in signal processing were using the models based on the sparse signal. Sub optimal strategies were used in these models to estimate the sparsest coefficients. In this work various algorithms were analyzed to address its optimal solutions. The sparsest solution can be found for the linear equations which are under determined. In this work, a complete study is carried out based on Compressive Sensing Matching Pursuit Back Tracking Iterative Hard Threshold (CMPBIHT) algorithm in the real-world scenario. As the BIHT algorithm may often fail to converge and its performance seems to be degraded if the conditions fail. To address these challenges, we have modified the BIHT algorithm to guarantee the convergence using the proposed method, even in this regime. Further the proposed CMPBIHT algorithm is evaluated and compared with the state of art techniques and it is observed that the proposed algorithm retains the similarities of the original algorithm. In this proposed model we have adopted the Compressive Sensing (CS) schemes along with Orthogonal Matching Pursuit (OMP). With this proposal we are able to solve the least squares problem for the new residual. We also investigated the reliability in sparse solutions along with compressive sensing techniques while decoding and over complete representations. An extensive research is carried out at the reconstruction side with the fundamental theme of CS, IHT and OMP techniques. The simulation results perform better efficiency at the reconstruction of the Gaussians signals by guaranteeing the productions in the residual error and noise. Further the proposed algorithm performs better at the reconstruction with nominal complexity in each of the iteration computationally.

中文翻译：

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压缩感知匹配追踪回溯迭代硬阈值算法的性能评估

如今，由于技术的进步，信号处理中的大多数应用都使用了基于稀疏信号的模型。在这些模型中使用了次优策略来估计最稀疏的系数。在这项工作中，分析了各种算法以解决其最佳解决方案。对于确定中的线性方程，可以找到最稀疏的解决方案。在这项工作中，在实际场景中基于压缩感知匹配追踪回溯迭代硬阈值（CMPBIHT）算法进行了完整的研究。由于BIHT算法可能经常无法收敛，并且如果条件失败，其性能似乎会降低。为了解决这些挑战，即使在这种情况下，我们也已对BIHT算法进行了修改，以确保所提出的方法的收敛性。此外，对提出的CMPBIHT算法进行了评估，并与现有技术进行了比较，结果发现，提出的算法保留了原始算法的相似性。在此提出的模型中，我们采用了压缩感知（CS）方案以及正交匹配追踪（OMP）。有了这个建议，我们就能解决新残差的最小二乘问题。我们还研究了稀疏解决方案的可靠性以及压缩感测技术，同时进行了解码和完整表示。在重建方面，以CS，IHT和OMP技术的基本主题进行了广泛的研究。通过保证产生的残留误差和噪声，仿真结果在重建高斯信号时具有更高的效率。