Weak wide-band signal detection method based on small-scale periodic state of Duffing oscillator

The conventional Duffing oscillator weak signal detection method, which is based on a strong reference signal, has inherent deficiencies. To address these issues, the characteristics of the Duffing oscillator's phase trajectory in a smallscale periodic state are analyzed by introducing the theory of stopping oscillation system. Based on this approach, a novel Duffing oscillator weak wide-band signal detection method is proposed. In this novel method, the reference signal is discarded, and the to-be-detected signal is directly used as a driving force. By calculating the cosine function of a phase space angle, a single Duffing oscillator can be used for weak wide-band signal detection instead of an array of uncoupled Duffing oscillators. Simulation results indicate that, compared with the conventional Duffing oscillator detection method,this approach performs better in frequency detection intervals, and reduces the signal-to-noise ratio detection threshold,while improving the real-time performance of the 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.     

Duffing振子微弱信号检测盲区消除及检测统计量构造

针对Duffing振子进行同频微弱信号检测时存在的检测盲区, 提出了一种策动力移相法予以消除. 结合微弱信号特性对检测盲区表达式进行分析, 得出了策动力与待测信号的“相差”位于检测盲区时的角度范围, 通过使策动力相位产生相移量π后实现对同频信号的检测, 实验证明了方法的可行性. 为了克服定性分析的不足和有效区分振子系统信号检测过程中出现的不同状态, 构造了一个基于类Halmiton系统的检测统计量, 并设计了基于该统计量的任意频率信号检测方法步骤, 方法的核心是以检测统计量出现极大值处所在的连续两个频点作为待测信号的频率范围. 在不同检测过程的仿真实验基础上, 给出了混沌、间歇混沌和大周期的检测统计量数值范围, 进而利用该数值范围作为判据实现了对任意频率信号的检测. 实验结果表明, 该方法不仅为系统状态提供了定量的判据准则, 而且提高了信号检测性能, 进一步完善了现有利用Duffing振子进行微弱信号检测的方法.     

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.     

Duffing振子微弱信号检测方法研究

在前期实验工作的基础上,从理论分析的角度,提出了利用Duffing振子从大周期态向混沌态的相变 作为判据的微弱周期信号检测方法,给出了检测原理,并论证了其可行性;从过渡带影响和检测概率两方面 将该方法与传统的检测方法进行了比较分析,并对两者的检测性能进行了仿真对比.分析和仿真结果都显示,相同条件下, Duffing振子从大周期态向混沌态的相变受过渡带影响更小,所提方法具有更好的检测性能. 实验数据还表明, Duffing振子检测微弱信号只能基于单向相变, 利用阵发混沌进行频差检测只适用于待测信号信噪比较高的情况. 关键词： Duffing 混沌 检测 过渡带     

Direction of arrival estimation for underwater acoustic target based on compressed sensing after blind reconstruction of array signal in frequency domain

The performance of direction of arrival(DOA) estimation based on compressed sensing(CS) decreases in the complex ocean marine environment.In order to tackle this problem,a method of DOA estimation for underwater acoustic target based on CS after blind reconstruction of array signal in frequency domain is proposed.Firstly,the received array data are transformed to frequency domain by Fourier transform and frequency domain wideband signal are divided into part overlapping multiple sub-band array signal.Secondly,each subband array signal are separated using plural blind source separation(BSS) method,the sub-band separated matrix and target signal can be estimated.Thirdly,the array signal in frequency domain are reconstructed according to the separated matrix and separated signals which were not noises.Fourthly,the sub-band spatial spectrum corresponding to the reconstructed array signal is obtained by CS beamforming method.Finally,the total spatial spectrum is achieved by summing the all sub-band spatial spectrum.And the target direction can be estimated by searching the peak value of the total spatial spectrum.The verification results of simulator data and sea measured data show that,under the same conditions,the target detection ability and direction precision of the proposed method is superior to the classical minimum variance distortionless response(MVDR) method,frequency domain CS method,BSS combined with MVDR method.The spatial spectrum energy of faint target signal is improved obviously,and the ability of the sonar to detect faint target is enhanced.     

基于耦合Duffing振子的局部放电信号检测方法研究

以双向环形耦合Duffing振子系统为对象, 研究脉冲信号激励下耦合振子间动力学行为变化特征时, 发现其与单向环形耦合Duffing振子系统类似, 在一定的参数条件下, 脉冲信号能引起其中一个振子与其他振子运动轨迹间出现短暂失同步的现象即瞬态同步突变现象. 基于这种现象, 提出了一种微弱脉冲信号检测的新方法, 用于检测强噪声背景中的局部放电脉冲信号. 实验测试表明, 利用本文方法对不同放电电极的局部放电脉冲信号进行检测时, 在低信噪比条件下可取得良好的检测效果, 进而扩展了现有的Duffing振子对非周期信号的检测范围及应用领域. 关键词： 耦合Duffing 振子 微弱信号检测 瞬态同步突变 局部放电脉冲信号     

盲重构频域阵列信号的压缩感知水声目标方位估计

针对复杂海洋环境条件下压缩感知水声目标方位估计性能下降的问题,利用盲源分离能够提高信噪比的优势,提出了一种盲重构频域阵列信号的压缩感知水声目标方位估计方法。首先将阵元域信号通过傅里叶变换方法得到多个子带阵列信号;然后对各个子带阵列信号进行复数域盲源分离得到子带解混矩阵和子带分离信号估计,并对子带分离信号进行属性分析和处理;再根据处理后的子带分离信号和子带解混矩阵重构子带阵列信号,对重构的子带阵列信号采用频域压缩感知方法进行空间谱估计,得到各个子带的空间谱;最后将各子带得到的空间谱进行求和,搜索求和后空间谱的峰值则可实现目标方位估计。模拟器数据和海上实测数据验证结果表明,同等条件下该方法的目标检测能力优于经典的最小方差无失真响应(Minimum Variance Distortionless Response,MVDR)方法、频域压缩感知(Compressed Sensing,CS)方法、盲源分离(Blind Source Separation,BSS)与MVDR相结合的方法(BSS+MVDR方法),测向精度更高,明显提高了弱目标信号的空间谱能量,增强了声呐检测弱目标的能力。      

基于Duffing振子的变尺度微弱特征信号检测方法研究

研究了Holmes型Duffing方程的混沌特性用于信号检测的方法,针对该方法只适用检测特定频率成分信号、方程参数选择不便以及噪声影响检测结果等情况,提出基于Duffing振子的变尺度微弱特征信号检测方法. 数值分析表明,该方法可以仅用一组确定的参数条件,检测任意频率、任意相位的特征信号.     

脉冲激励下环形耦合Duffing振子间的瞬态同步突变现象

研究非周期信号激励下Duffing振子动力学行为变化特征时,发现处于倍周期分岔的环形耦合Duffing振子系统,在一定的参数条件下,脉冲信号能引起其中一个振子与其他振子运动轨迹间出现短暂失同步的现象即瞬态同步突变现象.利用这种现象可以快速检测出强噪声背景中的微弱脉冲信号,从而扩展了现有的Duffing振子对非周期信号的检测范围及应用领域. 关键词： 瞬态同步突变 微弱信号检测 脉冲信号 Duffing振子     

基于扩展型Duffing振子的局部放电信号检测方法研究

目前, 小波阈值去噪法、数字滤波法、傅里叶频域变换法等常用的微弱信号检测方法所能达到的最低检测信噪比为-10 dB, 而双向环形耦合Duffing振子能达到的最低检测信噪比为-20 dB. 但是, 现场检测时常常会出现更低信噪比的放电脉冲信号, 因此现有检测方法就很难满足信号检测的实际需求. 为了有效解决该难题, 研究了一种扩展型Duffing振子的微弱脉冲信号检测的新方法. 该方法的主要思想是使用广义时间尺度变换, 将Duffing振子模型变换为扩展型Duffing振子模型, 有效扩展了微弱信号的频率检测范围. 仿真结果表明, 扩展型Duffing振子不仅具有良好的噪声免疫特性, 而且能有效检测到信噪比低至-40 dB的局部放电微弱脉冲信号, 进一步扩展了现有Duffing振子微弱信号检测方法的检测范围和应用领域.     

基于Kramers逃逸速率的Duffing振子广义调参随机共振研究

以二维Duffing振子的随机共振为研究对象,提出Duffing振子的广义调参随机共振. 以Kramers逃逸速率为基础,建立了Duffing振子随机共振的判别函数,阐述了Duffing振子在不同噪声强度及信号频率输入条件下的广义调参随机共振规律,并给出了Duffing振子广义调参随机共振的一般方法. 关键词： Duffing振子 随机共振 Kramers逃逸速率 广义参数调节     

基于混沌和随机共振的微弱信号检测

推导了分数阶线性振子系统响应的一阶稳态矩的频率不变性和相移特性, 并通过理论分析得出, 在随机共振机制下, 分数阶线性振子对系统响应一阶稳态矩的幅值具有放大作用. 构造Duffing混沌振子检测器, 利用混沌系统对参数摄动的敏感性以及对噪声的免疫能力实现弱信号检测. 数值模拟证实, 该方法可以有效地从噪声背景中将微弱正弦信号检测出来, 并且相对传统的混沌检测方法能显著降低信噪比检测门限.     

Decimative subspace-based parameter estimation methods of magnetic resonance spectroscopy based on prior knowledge

The method Hankel Total Least Squares (HTLS)-PK, which successfully incorporates prior knowledge of known signal poles into the method HTLS, has been proven to greatly improve the performance for parameter estimation of overlapping peaks of magnetic resonance spectroscopy (MRS) signal. In addition, decimation is also proposed as a way to increase the performance of subspace-based parameter estimation methods in the case of oversampling. Taking advantage of decimation in combination with prior knowledge to estimate the MRS signal parameters, two novel subspace-based parameter estimation methods, HTLSDSumPK and HTLSDStackPK, are presented in this paper. The experimental results and relevant analysis show that the methods HTLSDSumPK, HTLSDStackPK and HTLS-PK are slightly better than the method HTLS at low noise levels; however, the three prior-knowledge-incorporating methods, especially the method HTLSDSumPK, have much better performance than the method HTLS at high noise levels in the terms of robustness, estimated accuracy and computational complexity. Even if some inaccuracy of prior knowledge is considered, the method HTLSDSumPK also shows some advantages.     

基于Hilbert变换及间歇混沌的水声微弱信号检测方法研究

利用Duffing振 子从混沌到间歇混沌的相变及其对策动力和待检测信号频差较小的周期信号的敏感性, 研究了强海洋背景噪声下微弱周期信号的检测. 通过构造混沌振子列的方法对频率未知信号进行扫频, 从而提取待检测信号的频率范围, 最后利用希尔伯特变换, 实现对间歇混沌的包络检测, 并计算出待检测信号的频率. 计算机仿真与实测水声信号处理结果表明, 利用基于希尔伯特变换的间歇混沌振子对水声微弱信号检测, 其检测信噪比比一般的间歇混沌振子提高了至少4.4 dB, 验证了所提方法的有效性.     

Subspace-based MRS data quantitation of multiplets using prior knowledge

Accurate quantitation of Magnetic Resonance Spectroscopy (MRS) signals is an essential step before converting the estimated signal parameters, such as frequencies, damping factors, and amplitudes, into biochemical quantities (concentration, pH). Several subspace-based parameter estimators have been developed for this task, which are efficient and accurate time-domain algorithms. However, they suffer from a serious drawback: they allow only a limited inclusion of prior knowledge which is important for accuracy and resolution. In this paper, a new method is presented: KNOB-SVD and its improved variant KNOB-TLS. KNOB-SVD is a recently proposed method, based on the Singular Value Decomposition (SVD), which allows the use of more prior knowledge about the signal parameters than previously published subspace-based methods. We compare its performance in terms of robustness and accuracy with the performance of three commonly used methods for signal parameter estimation: HTLS, a subspace-based method which does not allow any inclusion of prior knowledge, except for the model order; HTLSPK(Delta fd(eq)), a subspace-based method obtained by incorporating in HTLS the prior information that the frequency differences between doublet components are known and the damping factors are equal; and AMARES, an interactive maximum likelihood method that allows the inclusion of a variety of prior knowledge. Extensive simulation and in vivo studies, using (31)P as well as proton MRS signals, show that the new method outperforms HTLS and HTLSPK(Delta fd(eq)) in robustness, accuracy, and resolution, and that it provides parameter estimates comparable to the AMARES ones.     

基于DEMON谱和LSTM网络的水下运动目标噪声基频检测*

传统的船舶辐射噪声基频检测方法不仅依赖大量的先验知识,而且对背景噪声非常敏感。为了提高目标识别的稳定性和精确性,本文提出了一种基于深度神经网络的基频检测算法。首先从多通道水听器信号中提取DEMON谱,然后直接将二维谱特征矩阵输入由CNN和LSTM构成的级联网络,最后通过稠密层输出实现对基频的估计。从仿真和外场试验数据得到如下结论：（1）深度网络能够实现无先验知识和不同信噪比条件下的基频检测,具有良好的泛化性能。（2）LSTM网络能够高效地从时序DEMON谱中提取统计特征,提高基频估计精度。（3）输入信号的时间长短会影响网络的检测精度,更长时间的信号能够获得更好的检测结果。     

Non-linear parameter estimation with Volterra series using the method of recursive iteration through harmonic probing

Volterra series provides a platform for non-linear response representation and definition of higher order frequency response functions (FRFs). It has been extensively used in non-parametric system identification through measurement of first and higher order FRFs. A parametric system identification approach has been adopted in the present study. The series response structure is explored for parameter estimation of polynomial form non-linearity. First and higher order frequency response functions are extracted from the measured response harmonic amplitudes through recursive iteration. Relationships between higher order FRFs and first order FRF are then employed to estimate the non-linear parameters. Excitation levels are selected for minimum series approximation error and the number of terms in the series is controlled according to convergence requirement. The problem of low signal strength of higher harmonics is investigated and a measurability criterion is proposed for selection of excitation level and range of excitation frequency. The procedure is illustrated through numerical simulation for a Duffing oscillator. Robustness of the estimation procedure in the presence of measurement noise is also investigated.     

Duffing振子系统周期解的唯一性与精确周期信号的获取方法

研究Duffing振子系统的周期解的唯一性与精确周期信号的获取方法.应用定性分析方法,获得了一类Duffing振子系统具有唯一周期解的必要条件,同时也得到了一类更广泛的非线性周期系统的周期解的唯一性.在一定条件下,给出了Duffing振子系统精确周期信号的获取方法。     

基于量子粒子群算法的自适应随机共振方法研究

为提升随机共振理论在微弱信号检测领域中的实用性,以随机共振系统参数为研究对象,提出了基于量子粒子群算法的自适应随机共振方法.首先将自适应随机共振问题转化为多参数并行寻优问题,然后分别在Langevin系统和Duffing振子系统下进行仿真实验.在Langevin系统中,将量子粒子群算法和描点法进行了寻优结果对比;在Duffing振子系统中,Duffing振子系统的寻优结果则直接与Langevin系统的寻优结果进行了对比.实验结果表明:在寻优结果和寻优效率上,基于量子粒子群算法的自适应随机共振方法要明显高于描点法;在相同条件下,Duffing振子系统的寻优结果要优于Langevin系统的寻优结果;在两种系统下,输入信号信噪比越低就越能体现出量子粒子群算法的优越性.最后还对随机共振系统参数的寻优结果进行了规律性总结.