Tang Mingyang, Wu Yafeng
College of Energy and Power, Northwestern Polytechnical University, Xi'an 710129, China.
Sensors (Basel). 2023 Oct 8;23(19):8325. doi: 10.3390/s23198325.
Currently, the widely used blind source separation algorithm is typically associated with issues such as a sluggish rate of convergence and unstable accuracy, and it is mostly suitable for the separation of independent source signals. Nevertheless, source signals are not always independent of one another in practical applications. This paper suggests a blind source separation algorithm based on the bounded component analysis of the enhanced Beetle Antennae Search algorithm (BAS). Firstly, the restrictive assumptions of the bounded component analysis method are more relaxed and do not require the signal sources to be independent of each other, broadening the applicability of this blind source separation algorithm. Second, the objective function of bounded component analysis is optimized using the improved Beetle Antennae Search optimization algorithm. A step decay factor is introduced to ensure that the beetle does not miss the optimal point when approaching the target, improving the optimization accuracy. At the same time, since only one beetle is required, the optimization speed is also improved. Finally, simulation experiments show that the algorithm can effectively separate independent and dependent source signals and can be applied to blind source separation of images. Compared to traditional blind source separation algorithms, it has stronger universality and has faster convergence speed and higher accuracy compared to the original independent component analysis algorithm.
目前，广泛使用的盲源分离算法通常存在收敛速度缓慢和精度不稳定等问题，并且大多适用于独立源信号的分离。然而，在实际应用中源信号并非总是相互独立的。本文提出了一种基于增强型甲虫触角搜索算法（BAS）的有界分量分析的盲源分离算法。首先，有界分量分析方法的约束假设更为宽松，不要求信号源相互独立，拓宽了这种盲源分离算法的适用性。其次，使用改进的甲虫触角搜索优化算法对有界分量分析的目标函数进行优化。引入了步长衰减因子，以确保甲虫在接近目标时不会错过最优点，提高了优化精度。同时，由于只需要一只甲虫，优化速度也得到了提高。最后，仿真实验表明，该算法能够有效地分离独立和相关源信号，并且可以应用于图像的盲源分离。与传统的盲源分离算法相比，它具有更强的通用性，与原始独立分量分析算法相比，具有更快的收敛速度和更高的精度。
