消散型同步的微弱周期信号检测及噪声影响分析
发布时间：2018-12-10 13:40
【摘要】：在混沌预测模型基础上,提出了消散型同步的混沌背景下微弱信号检测算法。采用径向基函数神经网络(RBFNN)拟合混沌模型,结合消散型同步实现混沌时间序列与混沌系统的同步,利用同步误差实现微弱信号的检测。以Rossler混沌系统为研究对象,验证了算法的可行性,研究了噪声对微弱信号检测的影响。仿真研究表明,该算法能检测各种频率的微弱信号,在一定条件下可检测到信杂比大于-110dB的微弱周期信号;若信噪比SNR≥0dB,噪声对微弱信号检测的影响很小;但若SNR-10dB,将检测不出微弱信号。在理论研究基础上,由MKS-CEC-Ⅲ新型混沌演化控制实验仪获取Coullet混沌时间序列,添加不同频率的微弱信号,利用该算法实现了不同频率微弱信号的检测,说明该算法适用于其他混沌系统。
[Abstract]:Based on the chaotic prediction model, a weak signal detection algorithm based on dissipative synchronization is proposed. The chaotic model is fitted by radial basis function neural network (RBFNN). The synchronization of chaotic time series and chaotic system is realized with dissipative synchronization. The weak signal is detected by synchronization error. Taking Rossler chaotic system as the research object, the feasibility of the algorithm is verified, and the influence of noise on weak signal detection is studied. Simulation results show that the algorithm can detect weak signals at various frequencies, and detect weak periodic signals with signal-to-clutter ratio greater than-110dB under certain conditions, and if SNR 鈮，
本文编号：2370668
[Abstract]:Based on the chaotic prediction model, a weak signal detection algorithm based on dissipative synchronization is proposed. The chaotic model is fitted by radial basis function neural network (RBFNN). The synchronization of chaotic time series and chaotic system is realized with dissipative synchronization. The weak signal is detected by synchronization error. Taking Rossler chaotic system as the research object, the feasibility of the algorithm is verified, and the influence of noise on weak signal detection is studied. Simulation results show that the algorithm can detect weak signals at various frequencies, and detect weak periodic signals with signal-to-clutter ratio greater than-110dB under certain conditions, and if SNR 鈮，
本文编号：2370668
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