基于自适应随机共振理论的太赫兹雷达信号检测方法

太赫兹雷达系统在差频信号频谱分析过程中,干扰噪声影响其测距能力.针对上述问题,提出基于自适应随机共振理论的太赫兹雷达信号检测方法,通过对含噪差频信号进行二次采样,利用自适应随机共振系统提取信号,进行尺度恢复完成测距计算.实验数据显示,不同测量距离时,相较于快速傅里叶变换法,输出信噪比的平均增益为9.684 d B,其中测量距离为1000 mm处,差频信号初始频谱值提高了64.1倍,系统信噪比增益为11.761 d B;相较于滤波法,在测量距离为1000 mm处信噪比增益最大,提高了70.56%;输入噪声强度为1—5 V之间时,输出信噪比曲线的曲率相对于滤波法降低了86.5%,其中噪声强度为5 V时信噪比增益最大,为14.018 d B.实验表明太赫兹雷达系统的测距能力大幅提高.     

A novel parameter-induced adaptive stochastic resonance system based on composite multi-stable potential model

Stochastic resonance (SR) is used widely as a weak signal detection method by using noise in many fields. In order to improve the weak signal processing capability of SR, a novel composite multi-stable model is proposed, which is constructed by the joint of the tristable model and the Gaussian Potential (GP) model. The SR system based on this model is constructed and the signal-to-noise ratio (SNR) is regarded as the index to measure the SR effect. The differential brain storm optimization (DBSO) algorithm is used to optimize the system parameters collaboratively to achieve parameter-induced adaptive SR. The influences of the system parameters V and R and the noise intensity D on the output response of SR system are analyzed under Gaussian white noise and α stable noise environments, and the advantages of the composite multi-stable SR system over the traditional tristable system are verified. For different levels of weak signals, the output performances of SR systems based on composite multi-stable model, traditional tristable model, composite tristable model are compared and analyzed. The results prove that the proposed model has better performance. Meanwhile, the adaptive detection of the multiple high-frequency weak signal is realized using the composite multi-stable SR system. The simulation results show that the proposed system has strong weak signal processing capability and good immunity to noise types, which widens the application range of SR in practical engineering.     

Research on detection method of multi-frequency weak signal based on stochastic resonance and chaos characteristics of Duffing system

Weak signal detection has been widely used in many fields such as military and national economy. Aiming at the problem that the traditional stochastic resonance (SR) method can’t obtain the signal amplitude when detecting weak signals, the frequency and amplitude of the weak signal are obtained by combining the SR and chaos characteristics of the two-dimensional Duffing system. Firstly, the effects of two-dimensional Duffing system parameters a, b, k, noise intensity D on the Kramers rate and signal-to-noise ratio (SNR) are analyzed under the Gaussian white noise environment. The results show that the damping ratio K can hinder the SR effect of the system to some extent. Secondly, to solve the misjudgment of the state method of the weak signal amplitude in the detection, the Lyapunov exponent is used to assure the threshold's range, and the threshold of the chaotic critical state is found. Finally, the paper gives the processes of frequency and amplitude detection of multiple high-frequency signals, which realizes the effective detection of the frequency and amplitude of multiple high-frequency signals in a Gaussian white noise environment, and successfully applies the method to the accurate detection of boundary voltage amplitude in electrical impedance tomography.     

Weak signal detection based on underdamped stochastic resonance with an exponential bistable potential

Stochastic resonance (SR) is a vital approach to detect weak signals submerged in strong background noise, which is useful for mechanical fault diagnosis. The underdamped bistable SR (UBSR) is a kind of the most used SR, however, their potential structures are deficient to match with the complicated and diverse mechanical vibration signals and their parameters are selected subjectively which probably resulting in poor performance of UBSR. To overcome these shortcomings, this paper proposes an underdamped SR with exponential potential (UESR) which is generalized by using a harmonic model and a Gaussian potential (GP) model. The dynamics in UESR system is evaluated by the signal-to-noise ratio (SNR) which represents the effectiveness of noise utilization. Then, the effects of system parameters on system performance are investigated by output SNR versus noise intensity D for different parameters. Finally, the proposed method is used to process bearing experimental data and further perform bearing fault diagnosis. The experimental results demonstrate that a larger output SNR and higher spectrum peaks at fault characteristic frequencies can be obtained by the proposed method compared with the UBSR method, which confirm the effectiveness of the proposed method.     

Improvement of signal-to-noise ratio by stochastic resonance in sigmoid function threshold systems, demonstrated using a CMOS inverter

Stochastic resonance (SR) has become a well-known phenomenon that can enhance weak periodic signals with the help of noise. SR is an interesting phenomenon when applied to signal processing. Although it has been proven that SR does not always improve the signal-to-noise ratio (SNR), in a strongly nonlinear system such as simple threshold system, SR does in fact improve SNR for noisy pulsed signals at appropriate noise strength. However, even in such cases, when noise is weak, the SNR is degraded. Since the noise strength cannot be known in advance, it is difficult to apply SR to real signal processing. In this paper, we focused on the shape of the threshold at which SR did not degrade the SNR when noise was weak. To achieve output change when noise was weak, we numerically analyzed a sigmoid function threshold system. When the slope around the threshold was appropriate, SNR did not degrade when noise was weak and instead was improved at suitable noise strength. We also demonstrated SNR improvement for noisy pulsed voltages using a CMOS inverter, a very common threshold device. The input-output property of a CMOS inverter resembles the sigmoid function. By inputting the noisy signal voltage to a CMOS inverter, we measured the input and output voltages and analyzed the SNRs. The results showed that SNR was effectively improved over a wide range of noise strengths.     

滤波器与随机共振结合检测微弱信号

基于双稳随机共振系统及滤波器的不同特性,本文提出了一种将两者结合起来检测微弱周期信号的方法,先用自适应前置滤波器对输入的弱周期信号及噪声进行滤波,再使其通过双稳随机共振系统,进而检测出弱信号。对比只有双稳随机共振的系统,仿真结果表明此时的输出信号中待测信号频谱幅度得到了很大的提高,且周围的干扰信号也得到了明显的削弱,即两者的结合使用可以更好的检测出微弱信号,这对强噪声背景下的信号检测有很强的实用性。     

Stochastic resonance characteristic analysis of new potential function under Levy noise and bearing fault detection

Based on the output saturation of classcial bistable stochastic resonance (CBSR), a new type of piecewise nonlinear bistable stochastic resonance (PNBSR) system is constructed. The mean signal-to-noise ratio gain is regarded as an index to measure the stochastic resonance phenomenon. The laws for the resonant output of piecewise nonlinear bistable system governed by l, c, a, b and D of Levy noise are explored under different characteristic index α and symmetry parameter β of Levy noise. The results show that the output of PNBSR system has increased 4?dB by comparing with the output signal-to-noise ratio of CBSR system. And the stochastic resonance phenomenon can be induced by adjusting the piecewise nonlinear system's parameters under any α or β of Levy noise. The interval of the parameters of system which induces good stochastic resonance is roughly the same. And the output signal waveform of resonance is very similar to the input signal waveform, which has some reference value for the signal recovery. Moreover, we can find the good stochastic resonance interval of the system parameters do not change with D of Levy noise under the different noise intensity D of Levy noise. On the basis of this, adjusting the intensity amplification factor D of Levy noise, which induces good stochastic resonance, and the interval does not change with α or β. At last, the piecewise nonlinear bistable system is applied to detect bearing fault signals, which achieves better performance compared with the classical bistable system.     

一阶线性系统的调参随机共振研究

分析了一阶线性系统在正弦和白噪声信号作用下的输出功率谱和信噪比.研究表明,加性噪声作用下的线性系统不存在传统意义上的随机共振,但却存在输出信噪比随系统参数非单调变化的调参广义随机共振现象.针对任意频率信号,分析了不同采样频率下的调参共振谱特性,得出适当增大采样频率有利于特征信号识别的结论.     

一种基于广义Duffing振子的水中弱目标检测方法

海洋环境中,在水下目标的线谱频率未知或者目标辐射噪声的连续谱很弱时,很难实现水中弱目标的准确检测,本文提出基于广义Duffing振子检测系统的水下目标辐射噪声检测方法.通过对传统周期扰动的Duffing振子信号检测系统的分析和推广,提出了一种可输入非周期、非平稳信号的广义Duffing振子检测系统,可检测输入的无先验信息目标信号.为实现广义Duffing振子系统运动状态的精确、有效判断,提出了一种相空间图形的离散分布列计算方法,通过类网格函数实现了利用统计复杂度对系统输出的嵌入式表征,从而实现了无先验信息时的水中弱目标的嵌入式检测.相同条件下与传统检测方法仿真对比可知,本文提出的方法可以检测到更低信噪比下的目标,并能满足水中检测实时性要求.     

变分模态分量降噪后重构的水声弱信号检测

针对能量检测法在低信噪比下对非合作水声探测信号的检测性能显著下降的问题,提出了一种组合变分模态分解和小波变换降噪重构的信号检测方法。以信号分解出的各个本征模态函数的近似熵与互相关系数比值作为分量分类参数,将所得分量分为信号分量、含噪信号分量与噪声分量,然后利用第二代小波变换对含噪信号分量降噪后与信号分量组成重构信号,最后对重构信号进行检测。数值仿真结果表明该方法可以在无先验信息的情况下对CW和LFM信号自适应降噪,信噪比0 dB以下时CW信号重构后信噪比提升约12 dB,宽带LFM信号信噪比提升约8～9 dB,有效提升了低虚警概率下信号的检测概率。湖试结果表明,虚警概率为0.1时检测概率可提升至0.9以上,验证了该方法的有效性。     

Stochastic resonance for signal-modulated pump noise in a single-mode laser

By adopting the gain-noise model of the single-mode laser in which with bias and periodical signals serve as inputs, combining with the effect of coloured pump noise, we use the linear approximation method to calculate the power spectrum and signal-to-noise ratio (SNR) of the laser intensity under the condition of pump noise and quantum noise cross-related in the form of δ function. It is found that with the change of pump noise correlation time, both SNR and the output power will occur stochastic resonance (SR). If the bias signal α is very small, changing the intensities of pump noise and quantum noise respectively does not lead to the appearance of SR in the SNR; while α increases to a certain number, SR appears.     

乘性和加性α稳定噪声环境下的过阻尼单稳随机共振现象

本文将α稳定噪声与单稳随机共振系统相结合,研究了乘性和加性α稳定噪声环境下的过阻尼单稳随机共振现象,探究了α稳定噪声特征指数α(0α2)、对称参数β(-1β1),单稳系统参数a及乘性α稳定噪声放大系数D对共振输出效应的作用规律.研究结果表明,在不同分布的α稳定噪声环境下,在一定范围内通过调节a或D均可诱导随机共振来实现单个或多个高、低频微弱信号的检测,且a和D分别存在一个最优值可使系统产生最佳的随机共振效应;不同α或β均可对系统共振输出效应产生规律性的影响,且α或β在高、低频微弱信号检测中的作用规律相同;在研究α稳定噪声环境下单、多频单稳随机共振现象时所得结论是相同的.本研究结果可为实现α稳定噪声环境下单稳随机共振系统参数的自适应调节奠定基础.     

Influence of the net gain on characteristic of stochastic resonance in a single-mode laser system

The phenomenon of stochastic resonance (SR) is found in a single-mode laser system driven by the colored pump noise with signal modulation and the quantum noise with cross-correlation between the real and imaginary parts. When the net gain a0 changes, it is found that, 1) the shape of the curve of the signal-tonoise ratio (SNR) versus the pump noise self-correlation time T exhibits a changing process of multiform SR, from single-peak SR to simultaneous existence of resonances and suppressions; 2) the curve of SNR versus signal frequency Ω experiences a complicated changing process from the monotonous descending to the simultaneous appearances of a maximum and a minimum, and finally to monotonous descending; 3)the curve of SNR versus cross-correlation coefficient between the real and imaginary parts of the quantum noise λq appears an acute single-peak SR. Therefore, the net gain a0 greatly influences the characteristic of SR of laser system.     

Stochastic resonance in a single-well system with exponential potential driven by Levy noise

Noise and potential function are vital to stochastic resonance (SR). This paper attempts to broaden the research of the SR and explore a better potential function. Based on the absolute and exponential potentials, a generalized exponential type single-well potential function is constructed. Then the characteristics of the corresponding exponential type single-well SR (ESR) system driven by Levy noise is analyzed numerically. Firstly, the effects of the characteristic index α, symmetric parameter β and noise intensity D of Levy noise on the input signal to noise ratio (SNRi) are investigated. Then, the effects of system parameters a, b, r and noise intensity D on the resonant output is explored. Finally, the ESR system is applied to the fault characteristic extraction of rolling element bearings. The simulation results show that the SR phenomenon is able to be excited by tuning the parameters a, b, r and D under different values of α and β. The larger b (or a) widens the parameter interval of a (or b) which can induce SR. The ESR system is able to solve the problem that the traditional systems fail to achieve SR due to the improper selection of parameters. In bearing fault detection, the detection effect of the ESR system is superior to the bistable SR system.     

一种基于新型间歇混沌振子的舰船线谱检测方法

为了实现低信噪比下未知频率的舰船辐射线谱的检测,对常规型间歇混沌振子列检测方法进行了改进,提出了一种基于适应步长型间歇混沌振子的信号检测方法.该方法可以只用一个Duffing振子,通过设定一组能够覆盖待测信号所在频段的求解步长序列,实现对未知频率、具有任意初相位的微弱周期信号的搜索检测.为进一步提高系统的弱信号检测性能,分析了Holmes型Duffing方程在不同频率内置策动力下对弱信号灵敏度的差异.综合理论分析和仿真研究结果给出了Duffing振子在内置策动力角频率为0.4 rad/s时对弱信号检测性能最佳,并据此对所采用的Duffing振子进行了优化;仿真结果表明,改进后的Duffing振子的弱信号检测性能提高了12 dB.最后将此方法应用于一组含有舰船辐射线谱的实船数据,结果表明此方法可以实现低信噪比下的未知频率微弱线谱检测.     

基于自适应滤波器的大气湍流噪声抑制

为了解决大气湍流效应所引起的光无线通信系统中的衰落问题,建立了与大气湍流噪声及其它加性噪声有关的信号模型,实现了最大输出信噪比可提高3-20dB的匹配滤波器。根据最大似然比准则,推导了强度调制/直接探测(IM/DD)的光无线通信系统的最佳判决门限。采用自适应滤波器实现了对探测门限中所含的信号和湍流的统计值的预测,该自适应门限探测技术对大气湍流噪声有很好的抑制作用,在强湍流情况下,可降低系统的误码率到10-5以下。     

基于小波变换的光混沌信号消噪与Lyapunov指数计算

针对动力学方程未知且信噪比小的光混沌信号,采用小波多分辨分解算法对其进行噪音消减.用Lorenz混沌信号对该算法的消噪效果进行了检验.提出利用互信息量法和Cao氏法来改进小数据量法在时间延迟和嵌入维数计算上存在的主观选择性,对经过噪音消减的Lorenz混沌信号利用此改进的小数据量法计算其最大Lyapunov指数.结果表明,信噪比可提高近10 dB左右,最大Lyapunov指数计算误差可减少近30%,并求得半导体放大器光混沌信号的最大Lyapunov指数为0.389 6.     

Study on the denoising method for the electromagnetic ultrasonic echoes from multiple interfaces

The electromagnetic ultrasound is used in the detection of interfaces of the adhesive multilayer structures to solve the unstable coupling problem in ultrasonic testing by traditional piezoelectric transducers. Based on the analysis of the transforming mechanism of electromag-netic ultrasound energy and the resultant dead zone from mutual inductance of the transducer, the wavelet filtering by soft-thresholding and adaptive noise canceling methods are used simul-taneously to the detected electromagnetic ultrasonic signals to overcome the drawbacks of the low signal to noise ratio （SNR） and the wide intrinsic dead zone of the transducer. Processed results in the interface detection of a three layered adhesive sample of steel and rubber materials demonstrate that the wavelet filtering enhances the SNR about 12dB while the adaptive noise canceling narrows the dead zone effectively.     

Implication of two-coupled tri-stable stochastic resonance in weak signal detection

Stochastic resonance(SR) has been proved to be an effective approach to extract weak signals overwhelmed in noise.However, the detection effect of current SR models is still unsatisfactory. Here, a coupled tri-stable stochastic resonance(CTSSR) model is proposed to further increase the output signal-to-noise ratio(SNR) and improve the detection effect of SR. The effects of parameters a, b, c, and r in the proposed resonance system on the SNR are studied, by which we determine a set of parameters that is relatively optimal to implement a comparison with other classical SR models.Numerical experiment results indicate that this proposed model performs better in weak signal detection applications than the classical ones with merits of higher output SNR and better anti-noise capability.