基于三稳态随机共振的ZPW-2000移频信号检测方法研究  

Tri-stable stochastic resonance based research on ZPW-2000 frequency shift signal detection methods

作　　者：武晓春[1] 刘欣然 WU Xiaochun;LIU Xinran(School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学自动化与电气工程学院,甘肃兰州730070

出　　处：《铁道科学与工程学报》2024年第8期3394-3405,共12页Journal of Railway Science and Engineering

基　　金：中国国家铁路集团有限公司基金资助项目(N2022G012);国家自然科学基金地区资助项目(61661027)。

摘　　要：针对ZPW-2000移频信号谐波干扰检测技术在抑制噪声时,不可避免损害有用信号的问题,提出三稳态随机共振系统,将部分噪声能量向移频信号能量转化,减少移频信号能量损失。首先,将移频信号的中心频率从较高频段搬移到较低频段,着重研究移频信号的有用频段。选取尺度变化系数为1 000,保证输入信号在尺度变化的过程中满足数值计算的稳定性,使尺度变换后的小参数信号满足绝热近似理论。其次,使用混沌麻雀算法(chaos sparrow search optimization algorithm, CSSOA)优化三稳态随机共振系统,得到输出信噪比(signal-to-noise ratios, SNR)最大时的系统参数a、b、c以及γ。然后,将搬移至低频区段的移频信号输入至优化后的三稳态随机共振系统,实现信号降噪。最后,对降噪信号采用复调制快速傅里叶算法(zoom fast fourier transformation, ZFFT)实现低频信号的检测。仿真结果表明:对比三稳态随机共振系统处理前后移频信号频谱图,显示频域特征增强,在降噪的同时不损害移频信号的能量;以-5 dB为步长将噪声注入至移频信号时,输出信噪比至少可提升16.54 dB;降噪后的信号通过ZFFT算法解调能够完成ZPW-2000移频信号低频信号的检测。对比经验模态分解算法、小波阈值算法等算法处理不同信噪比的含噪移频信号,验证混沌麻雀算法优化的三稳态随机共振系统输出信噪比较大,降噪效果较好,具有良好的抗干扰能力。研究结果为实现在抑制噪声的同时减少移频信号能量损失提供参考。In response to the inevitable damage to useful signals caused by the harmonic interference detection technology of ZPW-2000 frequency shift signals in suppressing noise,a tri-stable stochastic resonance system was proposed to convert some noise energy into frequency shift signal energy so as to reduce the energy loss of frequency shift signals.First,the center frequency of the frequency shift signal was shifted from the higher frequency band to the lower frequency band,with an emphasis on studying the useful frequency bands of the frequency shift signal.The scale variation coefficient of 1000 was selected to ensure that the input signal could satisfy the stability of numerical calculation in the process of scale change,and the small parameter signal after scale transformation could satisfy the adiabatic approximation theory.Second,the tri-stable stochastic resonance system was optimized using the Chaos Sparrow Search Optimization Algorithm(CSSOA),and system parameters a,b,c,andγwere obtained with the maximum output signal-to-noise ratio(SNR).Subsequently,the frequency shift signal shifted to the low frequency range was input to the optimized tri-stable stochastic resonance system to achieve signal noise reduction.Finally,the low frequency signal was detected with Zoom Fast Fourier Transformation(ZFFT).Simulation results reveal that from comparing the spectrograms of the shifted signal before and after processing by the tri-stable stochastic resonance system,it is evident that the frequency domain features are enhanced,and the energy of the shifted signal remains unharmed concurrently with the noise reduction.The output signal-to-noise ratio can be enhanced by at least 16.54 dB when noise is added to the shifted signal with a step size of-5 dB,and the demodulation of the shifted signal post-noise reduction via the ZFFT algorithm is capable of completing the detection of the low frequency signals of the ZPW-2000 frequency shift signals.By comparing the empirical modal decomposition algorithm,wavelet threshold algo

关 键 词：三稳态随机共振 ZPW-2000移频信号 尺度变换 输出信噪比 信号检测 

分 类 号：U284.28[交通运输工程—交通信息工程及控制]