基于稀疏表示与粒子群优化算法的非平稳信号去噪研究  

De-noising nonstationary signal based on sparse representation and particle swarm optimization

作　　者：叶华[1] 谭冠政[1] 李广 刘晓琼 李晋[4] 周聪[2] 朱会杰 Ye Hua;Tan Guanzheng;Li Guang;Liu Xiaoqiong;Li Jin;Zhou Cong;Zhu Huijie(School of Information Science and Engineering,Central South University,Changsha 410083,China;Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring（Central South University）,Ministry of Education,Changsha 410083,China;School of Geophysics and Measurement-control Technology,East China University of Technology,Nanchang 330013,China;Institute of Physics and Information Science,Hunan Normal University,Changsha 410081,China;PLA 63983 Army,Wuxi 214035,China)

机构地区：[1]中南大学信息科学与工程学院,湖南长沙410083 [2]有色金属成矿预测与地质环境监测教育部重点实验室(中南大学),湖南长沙410083 [3]东华理工大学地球物理与测控技术学院,武汉南昌330013 [4]湖南师范大学物理与信息科学学院,湖南长沙410081 [5]中国人民解放军63983部队,江苏无锡214035

出　　处：《红外与激光工程》2018年第7期318-325,共8页Infrared and Laser Engineering

基　　金：国家863计划(2014AA06A602);有色金属成矿预测与地质环境监测教育部重点实验室开放基金(2017YSJS09)

摘　　要：非平稳信号的去噪是信号处理中的热点和难点。文中以冲击原子作为稀疏表示基,构建了仅对人文噪声敏感的冗余字典。并使用粒子群优化算法对匹配追踪算法进行优化,提出了基于稀疏表示与粒子群优化算法的非平稳信号去噪方法。为检验方法的有效性,论文首先进行了针对性的仿真实验。然后将所述方法用于实测的大地电磁信号处理。结果表明,所述方法可以在保留有用信号的前提下,有效分离出类充放电噪声、脉冲噪声以及其它多种不规则噪声,显著提高非平稳信号的信噪比。It is difficult and important to de-noise nonstationary signal. To this end, a new noise attenuation method for nonstationary signal was proposed based on sparse representation and Particle Swarm Optimization（PSO）. A redundant dictionary which is insensitive to useful signal was developed for the representation of cultural noises. PSO was used to improve the search strategy of Matching Pursuit（MP）. Simulated experiments and real MT data were used to test the proposed scheme. As a conclusion,not only charge-discharge-like noise can be effectively removed, spikes and some other irregular noise can also be well suppressed. The apparent resistivity and phase curves obtained after applying our scheme are greatly improved over previous.

关 键 词：稀疏表示 非平稳信号 粒子群优化算法 噪声衰减 冗余字典 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]