摘要
非线性随机共振系统具有利用噪声增强微弱信号的能力,为强噪声背景下的信号检测开辟了新的途径。该文提出一种变参数随机共振(VPSR)模型,实现对非周期信号的有效检测、噪声去除和信号恢复。通过以恢复信号的拟合决定系数和互相关系数作为评判标准,研究分析了不同参数变化对系统输出的影响,分析结果表明该模型能有效地从噪声背景中恢复时变信号。该方法拓展了随机共振用于时变信号检测技术的领域,在时变信号检测和处理以及雷达通讯等方向有着一定的潜在应用价值。
The nonlinear stochastic resonance system has the ability to take advantage of background noise to enhance the weak signal among it. It provides the new approach to detect the weak signal embedded with heavy noise. This study proposes a new Varying Parameter Stochastic Resonance（VPSR） model. The model performs well in the detection of a time-varying signal with noise as well as the denoising and signal recovery. This study takes the determination coefficient and cross correlation coefficient as the criteria and analyzes the influence of different parameters variation on the system output. The simulation results indicate the model performs better in the time-varying signal recovery than the traditional one. The proposed method develops the area of time-varying signal detection with stochastic resonance which can be hoped to be widely used in the aperiodic signal processing, radar communication, etc. due to its superiority.
出处
《电子与信息学报》
EI
CSCD
北大核心
2015年第9期2124-2131,共8页
Journal of Electronics & Information Technology
基金
国家自然科学基金(51475441
11274300)资助课题
关键词
信号处理
变参数随机共振
时变信号
微弱信号探测
信号恢复
Signal processing
Varying Parameter Stochastic Resonance（VPSR）
Time-varying signal
Weak signal detection
Signal recovery
作者简介
通信作者：张海滨zhbzhbyr@gmail．com张海滨：男，1989年生，博士生，研究方向为信号处理、随机共振．
何清波：男，1980年生，博士，副教授，研究方向为故障诊断与状态监测、随机共振、流形分析．
孔凡让：男，1951年生，博士，教授，研究方向为故障诊断、信号处理．
