Abstract
Bat algorithm, as an optimization strategy of the observation matrix, has been widely used. Observation matrix has a direct impact on the reconstructed signal accuracy as a projection transformation matrix, and it has been widely used in various algorithms. However, for the traditional experimental process, randomly generated observation matrices often result in a larger reconstruction error and unstable reconstruction results. Therefore, it is a challenge to retain more feature information of the original signal and reduce reconstruction error. To obtain a more accurate reconstruction signal and less memory space, it is important to select an effective compression and reconstruction strategy. To solve this problem, an adaptive bat algorithm is proposed to optimize the observation matrix in this paper. For the adaptive bat algorithm, we design a dynamic adjustment strategy of the optimal radius to improve its global convergence ability. The results of our simulation experiments verify that, compared with other algorithms, it can effectively reduce the reconstruction error and has stronger robustness.
Highlights
IntroductionImages and video data are gradually increasing in contemporary life. To reduce the cost of data storage, transmission and processing, signals are often represented with less storage space at an acceptable level of distortion
With signals, images and video data are gradually increasing in contemporary life
We proposed an adaptive bat algorithm to optimize the observation matrix
Summary
Images and video data are gradually increasing in contemporary life. To reduce the cost of data storage, transmission and processing, signals are often represented with less storage space at an acceptable level of distortion. In [5], Wang et al expounded the advantages of the compressed sensing theory in solving the problem of information redundancy for the traditional signal acquisition process. They analyzed the construction of the compressed sensing measurement matrix from four aspects. Donoho et al [6] proposed a method for linear measurement of a given vector and returned it to the Euclidean accuracy range. They suggested that the observation matrix should satisfy the restricted isometry property (RIP), and
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