College of Mechanical and Electronic Engineering, Suzhou University, Suzhou 234000, China.
College of Computer and Information, Hohai University, Nanjing 211100, China.
Sensors (Basel). 2022 Jul 3;22(13):5018. doi: 10.3390/s22135018.
In view of the fact that most of the traditional algorithms for reconstructing underwater acoustic signals from low-dimensional compressed data are based on known sparsity, a sparsity adaptive and variable step-size matching pursuit (SAVSMP) algorithm is proposed. Firstly, the algorithm uses Restricted Isometry Property (RIP) criterion to estimate the initial value of sparsity, and then employs curve fitting method to adjust the initial value of sparsity to avoid underestimation or overestimation, before finally realizing the close approach of the sparsity level with the adaptive step size. The algorithm selects the atoms by matching test, and uses the Least Squares Method to filter out the unsuitable atoms, so as to realize the precise reconstruction of underwater acoustic signal received by the sonar system. The experimental comparison reveals that the proposed algorithm overcomes the drawbacks of existing algorithms, in terms of high computation time and low reconstruction quality.
鉴于大多数用于从低维压缩数据中重建水下声信号的传统算法都是基于已知稀疏性的，因此提出了一种稀疏自适应和变步长匹配追踪（SAVSMP）算法。首先，该算法使用约束等距特性（RIP）准则来估计稀疏度的初始值，然后采用曲线拟合方法来调整稀疏度的初始值，以避免低估或高估，最后实现与自适应步长的稀疏度级别的紧密逼近。该算法通过匹配测试选择原子，并使用最小二乘法滤除不合适的原子，从而实现对声纳系统接收到的水下声信号的精确重建。实验比较表明，该算法克服了现有算法计算时间长和重建质量低的缺点。
