摘要
α噪声下的频谱感知成为近年来的研究热点,该噪声的统计模型具有明显的脉冲性和拖尾性,并且在微弱信号条件下,信号特征不够明显。为此提出了基于随机共振的最大相关熵频谱感知方法,该方法通过随机共振模型中粒子在双势阱间的跃迁,将α噪声的部分能量转移到信号中,以提高信号的输出信噪比。采用最大相关熵方法构建高阶统计量,检测随机共振后的输出信号,并联合共轭梯度下降法获取最佳目标函数,实现频谱感知。仿真结果表明,该算法在低信噪比条件下能够有效提高检测性能。
Spectrum sensing under α noise has become a hot topic in recent years.The statistical model of this noise has obvious impulse and trailing characteristics.The signal characteristics are not obvious enough under weak signal conditions.To this end,the maximum generalized correntropy spectrum sensing method based on stochastic resonance is proposed.This method uses the transition of particles in the stochastic resonance model between the two potential wells to transfer part of the energy of alpha noise into the signal to improve the signal output signal-to-noise ratio.The maximum generalized correntropy method is utilized to construct high-order statistics for spectrum sensing,detect the output signal after stochastic resonance and combine conjugate gradient descent method to achieve the optimal objective function.The simulations results demonstrate that the proposed algorithm can effectively improve the detection performance under the condition of low signal-to-noise ratio.
作者
李如雪
鲁进
罗聪
LI Ruxue;LU Jin;LUO Cong(School of Information Science and Engineering,Yunnan University,Kunming 650500,China;Yunnan Provincial Key Laboratory of Internet of Things Technology and Application in Universities,Kunming 650500,China)
出处
《数据采集与处理》
CSCD
北大核心
2023年第6期1342-1352,共11页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(61701432)
云南大学研究生科研创新项目(2021Y265)。
关键词
频谱感知
随机共振
α噪声
共轭梯度下降法
最大相关熵
spectrum sensing
stochastic resonance
α noise
conjugate gradient descent algorithm
maximum generalized correntropy
