一种随机共振智能融合信号滤波算法研究  

Research on Stochastic Resonance Intelligent Fusion Signal Filtering Algorithm

作　　者：王君红[1] 孙志坚 高天琳 刘宝[1] WANG Jun-hong;SUN Zhi-jian;GAO Tian-lin;LIU Bao(School of Control Science and Engineering,China University of Petroleum(East China),Qingdao Shandong 266580,China)

机构地区：[1]中国石油大学(华东)控制科学与工程学院,山东青岛266580

出　　处：《计算机仿真》2024年第12期388-394,共7页Computer Simulation

基　　金：中央高校基本科研业务费专项资金资助(20CX05006A);国家自然科学基金资助项目(60775052);中石油重大科技项目(ZD2019-183-007)。

摘　　要：针对井下声波信号在强噪声干扰下而难以解析的问题,提出一种奇异值分解与随机共振融合的信号处理算法。该算法首先利用奇异值分解进行初步处理,在信号重构时引入奇异值下降速度进行改进,进而提高确定奇异值阈值的准确性。随后将处理后的信号输入级联随机共振系统,达到信号滤波的目的,为了更加准确地确定系统参数,引入人工鱼群算法(AFSA)进行随机共振的参数寻优,提高了算法的信号滤波效果。实验结果表明,改进奇异值分解与优化级联随机共振融合的信号处理算法相比其它几种经典算法滤波效果更强,在强噪声背景下,信噪比增益可达15.235dB,提高了处理井下声波信号的输出信噪比。Aiming at the problem that it is difficult to parse the acoustic signal under the interference of strong noise,a signal processing algorithm combining improved singular value decomposition and optimized cascaded stochastic resonance was proposed.The algorithm first utilizes singular value decomposition for preliminary processing,and introduces the rate of singular value descent during signal reconstruction for improvement,thereby enhancing the accuracy of determining singular value thresholds.Subsequently,the processed signal is input into a cascaded stochastic resonance system to achieve signal filtering.In order to more accurately determine the system parameters,the artificial fish swarm algorithm(AFSA)is introduced for parameter optimization of stochastic resonance,which improves the signal filtering effect of the algorithm.The experimental results show that the improved singular value decomposition and optimized cascades stochastic resonance fusion signal processing algorithm have better filtering effect than other classical algorithms,and the SNR gain can reach 15.235dB under strong noise,which improves the output SNR of the downhole acoustic signal processing.

关 键 词：信号处理 奇异值分解 随机共振 人工鱼群算法 

分 类 号：TN911[电子电信—通信与信息系统]