混沌背景下无线网络微弱信号自动检测仿真
|
摘要
当前信号检测模型在进行网络微弱信号检测过程中,检测结果存在信号频率分辨较低、误码率相对较高等问题,针对上述问题,提出一种基于经验模态分解的无线网络微弱信号检测模型。采用经验模态分解法对采集到的无线网络观测信号进行分解,对分解获得的多个包含原始信号局部特征的模态分量进行相空间重组,以重组结果构建微弱信号的预测检验模型。将高斯核函数融合于加权最小二乘法,进一步减小模型误差,求解出模型参数。利用模型参数估计信号预测值并计算预测误差,从模型预测误差中检验观测信号中是否含有微弱信号。仿真证明,所提模型对信号频率的分辨率相对较高,检测性能优于对比模型。
A weak signal detection model in wireless network based on empirical mode decomposition was presented. Firstly,the empirical mode decomposition method was used to decompose collected wireless network observation signals and modal components including local features of original signals were recombined in phase space. Moreover,recombination results were used to build the model of predicting and testing weak signal. In addition,Gaussian kernel function was integrated into the weighted least squares method so as to reduce model error and obtain model parameters. Finally,the model parameters were used to estimate the signal prediction value and calculate the prediction error. Thus,the model prediction error was used to check whether the observation signal contained weak signals. Simulation proves that the proposed model has a relatively high resolution for signal frequency. Meanwhile,the detection performance is better than that of contrast model.
A weak signal detection model in wireless network based on empirical mode decomposition was presented. Firstly,the empirical mode decomposition method was used to decompose collected wireless network observation signals and modal components including local features of original signals were recombined in phase space. Moreover,recombination results were used to build the model of predicting and testing weak signal. In addition,Gaussian kernel function was integrated into the weighted least squares method so as to reduce model error and obtain model parameters. Finally,the model parameters were used to estimate the signal prediction value and calculate the prediction error. Thus,the model prediction error was used to check whether the observation signal contained weak signals. Simulation proves that the proposed model has a relatively high resolution for signal frequency. Meanwhile,the detection performance is better than that of contrast model.
引文
[1]张勇亮,李国林,张晓瑜.基于并联自适应随机共振的微弱信号检测方法[J].计算机工程与设计,2017,38(5):1324-1330.
[2]郝静,等.调参随机共振在超高频微弱信号检测中的应用[J].计算机应用,2016,36(9):2374-2380.
[3]郭黎利,高飞,孙志国.基于局部量化观测的微弱信号分布式Rao检测[J].哈尔滨工程大学学报,2016,37(10):1438-1442.
[4]李国正,张波.基于Duffing振子检测频率未知微弱信号的新方法[J].仪器仪表学报,2017,38(1):181-189.
[5]姜烁,等.改进高阶Vanderpol振子在微弱信号检测中的应用[J].电讯技术,2017,57(6):678-684.
[6]孙忠阁.无线网络信道传输奇异类信号检测研究[J].计算机仿真,2017,34(4):406-409.
[7]刘剑鸣,等.类Liu系统在水声微弱信号检测中的应用研究[J].物理学报,2016,65(7):40-50.
[8]缑新科,马艳.基于随机共振的混合频率微弱信号检测[J].兰州理工大学学报,2016,42(2):97-100.
[9]宋菲菲,魏自明,习敬伟.新SH-COS混沌弱信号检测系统设计[J].高技术通讯,2016,26(12):1006-1013.
[10]孙文军,等.基于系统一阶摄动解主频功率比的弱信号检测方法[J].电子与信息学报,2016,38(1):160-167.
[2]郝静,等.调参随机共振在超高频微弱信号检测中的应用[J].计算机应用,2016,36(9):2374-2380.
[3]郭黎利,高飞,孙志国.基于局部量化观测的微弱信号分布式Rao检测[J].哈尔滨工程大学学报,2016,37(10):1438-1442.
[4]李国正,张波.基于Duffing振子检测频率未知微弱信号的新方法[J].仪器仪表学报,2017,38(1):181-189.
[5]姜烁,等.改进高阶Vanderpol振子在微弱信号检测中的应用[J].电讯技术,2017,57(6):678-684.
[6]孙忠阁.无线网络信道传输奇异类信号检测研究[J].计算机仿真,2017,34(4):406-409.
[7]刘剑鸣,等.类Liu系统在水声微弱信号检测中的应用研究[J].物理学报,2016,65(7):40-50.
[8]缑新科,马艳.基于随机共振的混合频率微弱信号检测[J].兰州理工大学学报,2016,42(2):97-100.
[9]宋菲菲,魏自明,习敬伟.新SH-COS混沌弱信号检测系统设计[J].高技术通讯,2016,26(12):1006-1013.
[10]孙文军,等.基于系统一阶摄动解主频功率比的弱信号检测方法[J].电子与信息学报,2016,38(1):160-167.