| 吴宸之,李 炜,房 琪,王 晶.基于小波包混合优化的欠定盲源分离方法[J].电子测量与仪器学报,2023,37(9):213-224 |
| 基于小波包混合优化的欠定盲源分离方法 |
| Underdetermined blind source separation method based on wavelet packet hybrid optimization |
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| DOI： |
| 中文关键词: 欠定盲源分离 小波包变换 灰狼优化算法 |
| 英文关键词:underdetermined blind source separation wavelet packet transform gray wolf optimization algorithm |
| 基金项目:安徽省高校自然科学研究项目(KJ2021A1524)、安徽省高校协同创新项目( GXXT-2021-050)、安徽省高校杰出青年科研项目(2022AH020065)、安徽工程大学本科生科学研究项目(2022DZ01)、电子制约技术安徽省重点实验室开放基金(ERKL2023KF01)项目资助 |
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| Author | Institution |
| Wu Chenzhi | 1. Key Laboratory of Advanced Perception and Intelligent Control for High-end Equipment, Ministry of Education, Anhui Polytechnic University,2. Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University |
| Li Wei | 1. Key Laboratory of Advanced Perception and Intelligent Control for High-end Equipment, Ministry of Education, Anhui Polytechnic University,2. Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University |
| Fang Qi | 1. Key Laboratory of Advanced Perception and Intelligent Control for High-end Equipment, Ministry of Education, Anhui Polytechnic University,2. Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University |
| Wang Jing | 1. Key Laboratory of Advanced Perception and Intelligent Control for High-end Equipment, Ministry of Education, Anhui Polytechnic University,2. Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University |
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| 中文摘要: |
| 为解决欠定盲源分离问题,提出一种基于小波包混合优化的欠定盲源分离方法。 该方法采用小波包变换将观测信号分
解,将观测信号的维数进行扩展,利用互相关系数值剔除冗余的信号分量,欠定盲源分离问题得到转化。 接着使用贝叶斯信息
准则下的奇异值分解方法估计源信号数目,通过白化过程对信号降维。 最后,引入鲸鱼优化算法中的螺旋泡网狩猎行为与莱维
飞行策略,对灰狼优化算法进行改进,将改进后的混合灰狼优化算法与独立成分分析算法相结合,实现重构正定白化信号的分
离,从而得到源信号的近似估计。 通过仿真实验对算法性能进行测试,结果验证所提方法的可行性和有效性。 |
| 英文摘要: |
| To solve the problem of underdetermined blind source separation (UBSS), an UBSS method based on wavelet packet hybrid
optimization is presented. The method, adopting wavelet packet transform, decomposes the observed signal, expands the dimension of
the observed signal, and removes redundant signal components with the cross-correlation value, transforming the problem of UBSS.
Then, with the singular value decomposition method under the Bayesian information criterion, the number of source signals is estimated,
and the signal dimension is reduced through the whitening process. At last, the spiral bubble net hunting behavior and levy flight strategy
in the whale optimization algorithm (WOA) are introduced to improve the gray wolf optimization algorithm, and the improved hybrid gray
wolf optimization algorithm is integrated with the independent component analysis algorithm to separate the reconstructed positive definite
whitening signals, and rewarding separation performance is achieved. The performance of the algorithm is tested by simulation
experiments, and the results show the feasibility and effectiveness of the proposed method. |
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