摘要
为解决在强噪声背景下获取超声信号的难题,基于粒子群优化算法和稀疏分解理论提出一种强噪声背景下微弱超声信号提取方法.该方法将降噪问题转换为在无穷大参数集上对函数进行优化的问题,首先以稀疏分解理论和超声信号的结构特点为依据构建了粒子群优化算法运行所需要的目标函数及去噪后信号的重构函数,从而将粒子群优化算法和超声信号降噪联系在一起;然后根据粒子群优化算法可以在连续参数空间寻优的特点建立了用于匹配超声信号的连续超完备字典,并采用改进的自适应粒子群优化算法在该字典中对目标函数进行优化;最后根据对目标函数在字典上的优化结果确定最优原子,并利用最优原子按照重构函数重构出降噪后的超声信号.通过对仿真超声信号和实测超声信号的处理,结果表明本文提出的方法可以有效提取信噪比低至-4 dB的强噪声背景下的微弱超声信号,且和基于自适应阈值的小波方法相比本文方法表现出更好的降噪性能.
In order to solve the problem of extracting ultrasonic signals from strong background noise, a novel method,which is termed APSO-SD algorithm and based on improved adaptive particle swarm optimization（APSO） and sparse decomposition（SD） theory, is proposed in this paper. This method can convert the ultrasonic signal denoising problem into optimizing the function on the infinite parameter set. First, based on the sparse decomposition theory and the structural characteristics of ultrasonic signal, the objective function of particle swarm optimization algorithm and the reconstruction algorithm of the denoised signal are constructed, so that particle swarm optimization and ultrasonic signal denoising can be combined. Second, in order to improve the robustness of the proposed approach, an APSO algorithm is proposed. What is more, because particle swarm optimization algorithm can be used to optimize in continuous parameter space, and according to the empirical characteristics of the ultrasonic signals used in practical engineering, a continuous super complete dictionary for matching ultrasonic signals is established. Since the super complete dictionary is continuous, there are an infinite number of atoms in the established dictionary. The redundancy of dictionaries is enhanced by the method in this paper. Based on the fact that the inner product of the optimal atom and the ultrasonic signal is one and the inner product of the noise and the optimal atom is zero in the established dictionary, the objective optimization function of APSO-SD algorithm is established. Finally, the optimal atom is determined based on the optimization result of the objective function. In this way, the denoising ultrasonic signal can be reconstructed by using the optimal atom according to the reconstruction algorithm. The processing results of simulated ultrasonic signals and measured ultrasonic signals show that the proposed method can effectively extract weak ultrasonic signals from strong background noise whose signal-to-noise ratio is lowest, as low as-4 dB. In addition, compared with the adaptive threshold based wavelet method, the proposed method in this paper shows the good denoising performance. In this paper, it is demonstrated that the problem of ultrasonic signal denoising can be transformed into the optimization of constraint functions. Furthermore, the ability of the proposed APSO-SD algorithm to accurately recover signals from noisy acoustic signals is better than that of the common wavelet method.
作者
王大为
王召巴
Wang Da-Wei;Wang Zhao-Ba(School of Information and Communication Engineering,North University of China,Taiyuan 030051,China;School of Physics and Information Engineering,Shanxi Normal University,Linfen 041000,China)
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2018年第21期52-64,共13页
Acta Physica Sinica
基金
国家自然科学基金(批准号:11604304)
山西省科技攻关项目(批准号:201603D121006-1)
山西省回国留学人员科研资助项目(批准号:2016-084)
山西省高等学校科技创新项目(批准号:201657)资助的课题~~
关键词
超声回波
信号降噪
稀疏分解
粒子群优化
ultrasonic echo
signal denoising
sparse decomposition
particle swarm optimization
