红外与激光工程, 2018, 47 (7): 0726005, 网络出版: 2018-08-30   

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
作者单位
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
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。
Abstract
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.
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叶华, 谭冠政, 李广, 刘晓琼, 李晋, 周聪, 朱会杰. 基于稀疏表示与粒子群优化算法的非平稳信号去噪研究[J]. 红外与激光工程, 2018, 47(7): 0726005. Ye Hua, Tan Guanzheng, Li Guang, Liu Xiaoqiong, Li Jin, Zhou Cong, Zhu Huijie. De-noising nonstationary signal based on sparse representation and particle swarm optimization[J]. Infrared and Laser Engineering, 2018, 47(7): 0726005.

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