一种非线性动力学降噪法应用于海底探雷抗混响的探讨

文章所述降噪法建立在背景信号能够用低维动力学系统建模的基础之上，利用待分离信号与干扰背景的频谱差异，通过连续拟合的两层RBF网来构造非线性逆滤波器以达到信号与背景分离的目的。本文给出了分别以仿真混沌信号和实测混响信号为背景与单频脉冲信号相分离的示例，证明了该算法在混响背景下实现信号分离的可行性(分离前后信混比提高了约16dB)，分析了算法有效使用所必需的频谱差异条件(不小于0．15个圆周频率)，为抑制混响提供了一条新的思路。     

基于几何结构的非线性动力学信号分离方法

介绍了时间序列非线性动力学分析的理论基础及三种基于重构点集几何结构的信号分离方法.     

基于混沌理论的海底混响中目标回波提取

提出了基于混沌理论的混响中目标回波提取新方法。该方法主要得益于一种新的预测模型,该模型基于径向基函数神经网络,综合利用了时间序列的前向和后向预测,解释了该模型用于混沌信号分离的基本原理,用几种混沌时间序列分析了该模型用于混沌信号建模和谐波信号提取的性能。该方法用于湖试混响中目标回波提取的结果表明:该模型可以用于提取信混比不小于1dB的目标回波。     

基于非线性动力学的声呐回波特征提取

文章介绍了以多项式拟合的方法进行信号重构的非线性动力学模型,选取少量的拟合系数作为衡量信号与干扰的特征差异的表征量,构成“动力学分类器”。在完善了用于相空间重构的动力学模型基础上,确定了用于目标回波的主要非线性系数。数据分析结果表明了该方法的有效性,得出的结论表明主动声纳回波具有与混响不同的非线性动力学特征,且不同目标之间的非线性动力学特征也不相同,这为回波的非线性动力学建模及其用于检测与分类奠定了基础。     

一种新的非线性系统脊骨线提取方法

脊骨线对于非线性系统的判断、描述和参数识别,具有重要的意义;而现有的脊骨线提取方法大多理论上较为复杂,且有局部线性化误差较大、数据波动剧烈等问题,限制了工程应用。提出一种新的非线性系统脊骨线提取方法,该方法利用非线性系统自由振动响应的一次谐波分量,计算谐波信号峰值点的瞬时频率,可以方便地提取脊骨线;这种新的非线性系统脊骨线提取方法理论明确、简单,计算方便,易于工程应用。通过三个数值算例说明了该方法的有效性,同时将此方法应用于非线性描述,并综合讨论了非线性描述的几何方法;并基于此方法进行了非线性系统参数识别,取得了良好的效果。     

混响背景下主动声纳接收信号的可分离性探讨

混响背景下主动声纳的接收信号是否具有可分离性是盲分离在主动声纳抗混响中获得应用的前提条件。在建立混响背景下的接收信号模型的基础上,通过模型中源信号间相关性的分析,提出了一种接收信号是否具有可分离性的判断准则：在绝大多数时延条件下相关系数很小的发射信号,其混响背景下的接收信号具有可分离性。通过主动声纳的两种发射信号（CW和LFM）的仿真和海试实验研究对这一准则的可行性进行了检验。对于符合可分离性判断准则LFM信号,实验结果验证了其混响背景下接收信号的可分离性。     

一种改进的EMD降噪方法

提出一种改进的基于经验模态分解（EMD）的降噪方法,将基于EMD阈值降噪方法和Savitzky-Golay滤波降噪方法相结合,改进的方法分别将经过EMD分解后得到的噪声信号的高频模态函数用阈值降噪的方法,低频模态函数用Savitzky-Golay滤波降噪的方法,这样既能保持信号的高频部分,又能较好地保持信号低频部分的光滑特性。仿真实验表明,改进EMD方法的降噪性能要优于单独使用EMD阈值方法或Savitzky-Golay滤波方法。     

一种基于同伦非线性模型的语音盲分离方法

采用同伦非线性模型对语音信号进行建模,将非线性可预测性作为盲源分离的准则,推导了基于同伦模型的盲源分离算法,成功地实现了语音信号的分离。这种方法是对基于线性预测模型盲源分离算法的推广,它既适用于分离采用线性预测模型建模的信号,也可以分离采用同伦非线性模型建模的信号(如语音)。理论分析和仿真表明,这种方法比基于线性可预测性的盲分离算法具有更广的适用范围,对于语音信号的分离,此种算法具有更快的收敛速度。     

一种非平稳、非线性振动信号检测方法的研究

排列熵作为一种统计测度，是通过相空间重构来衡量时间序列复杂度的一个参数。为研究机械设备非平稳信号的非线性动力学特征，将排列熵引入机械设备故障诊断，研究了不同工作状态下滚动轴承振动信号的排列熵和嵌入维数与时间延迟对计算机械振动信号排列熵的影响，观察设备状态变化及振动冲击对排列熵的影响并对其进行了分析。结果表明：排列熵可以有效地检测出机械设备状态的变化，可以作为检测机械设备状态变化的一个参数。     

一种非线性误差修正方法

对于采用光栅做位移传感器的长度测量仪器,为提高仪器测量准确度,多采用误差修正的方法,来减小仪器示值误差。本文介绍了一种非线性误差修正方法,该方法在国外有的仪器上已被采用,作者也做过试验,验证了方法的可行性。     

融入频域先验信息压缩感知方法的主动声呐回波信号处理

针对弱信噪比时的水下主动声呐回波信号处理问题,从主动声呐入射信号和目标回波信号的先验信息出发,与压缩感知理论相结合,提出了融入频域先验信息的压缩感知方法（Compressed Sensing based on Fequency Prior Information,CSFPI）。针对主动声呐入射信号,获取其频域先验信息,将其作为“原子”融入信号的稀疏分解过程,构建完备频域先验稀疏矩阵。主动声呐回波信号可以等效为携带目标信息入射信号的线性叠加,将该特性与传统正交匹配追踪（Orthogonal Matching Pursuit,OMP）算法结合,形成基于“块”的正交匹配信号重构算法。采用CSFPI方法处理仿真信号来验证理论方法的正确性。进一步,通过主动声呐发射接收装置获取湖上实测数据,用CSFPI方法进行处理。当压缩比为50%、信噪比低至-5 dB时,重构信号的匹配率高于70%。实验结果表明了CSFPI算法在处理低信噪比声呐信号时的有效性。     

一种基于谱减的声纳噪声自适应消除方法

噪声自适应消除是声纳信号处理的重要研究内容之一.传统的噪声自适应抵消算法需要单独的阵列(阵元)以获得不含期望信号的参考噪声信号,这在实际工程应用中往往是不现实的.提出在不增加阵元的情况下,通过相邻两个阵元输出信号进行加权处理,合成一路不包含给定方向信号的噪声信号;同时,借鉴语音信号处理中普遍应用的谱减降噪处理方法,达到...     

一种主动声呐作用距离指标试验评定方法

针对主动声呐作用距离指标试验评定考核,阐述主动声呐作用距离指标定义,明确主动声呐作用距离是主动声呐满足规定检测概率的作用距离,提出一种主动声呐作用距离指标试验评定方法,即根据主动声呐以重复的单次发射和检测方式工作的特点,首先以滑动窗采样周期的形式检验声呐检测目标的概率,其次规定3次试验航路至少2次达标的标准,以验证基于发现概率的声呐作用距离,进而验证声呐技术规范规定的检测概率,使得海上试验结论可信度高。该评定方法可作为主动声呐作用距离指标评定考核的一种依据,为声呐装备试验、训练提供参考。     

Accelerated mesh sampling for the hyper reduction of nonlinear computational models

In nonlinear model order reduction, hyper reduction designates the process of approximating a projection‐based reduced‐order operator on a reduced mesh, using a numerical algorithm whose computational complexity scales with the small size of the projection‐based reduced‐order model. Usually, the reduced mesh is constructed by sampling the large‐scale mesh associated with the high‐dimensional model underlying the projection‐based reduced‐order model. The sampling process itself is governed by the minimization of the size of the reduced mesh for which the hyper reduction method of interest delivers the desired accuracy for a chosen set of training reduced‐order quantities. Because such a construction procedure is combinatorially hard, its key objective function is conveniently substituted with a convex approximation. Nevertheless, for large‐scale meshes, the resulting mesh sampling procedure remains computationally intensive. In this paper, three different convex approximations that promote sparsity in the solution are considered for constructing reduced meshes that are suitable for hyper reduction and paired with appropriate active set algorithms for solving the resulting minimization problems. These algorithms are equipped with carefully designed parallel computational kernels in order to accelerate the overall process of mesh sampling for hyper reduction, and therefore achieve practicality for realistic, large‐scale, nonlinear structural dynamics problems. Conclusions are also offered as to what algorithm is most suitable for constructing a reduced mesh for the purpose of hyper reduction. Copyright © 2016 John Wiley & Sons, Ltd.     

On the use of modal derivatives for nonlinear model order reduction

Modal derivative is an approach to compute a reduced basis for model order reduction of large‐scale nonlinear systems that typically stem from the discretization of partial differential equations. In this way, a complex nonlinear simulation model can be integrated into an optimization problem or the design of a controller, based on the resulting small‐scale state‐space model. We investigate the approximation properties of modal derivatives analytically and thus lay a theoretical foundation of their use in model order reduction, which has been missing so far. Concentrating on the application field of structural mechanics and structural dynamics, we show that the concept of modal derivatives can also be applied as nonlinear extension of the Craig–Bampton family of methods for substructuring. We furthermore generalize the approach from a pure projection scheme to a novel reduced‐order modeling method that replaces all nonlinear terms by quadratic expressions in the reduced state variables. This complexity reduction leads to a frequency‐preserving nonlinear quadratic state‐space model. Numerical examples with carefully chosen nonlinear model problems and three‐dimensional nonlinear elasticity confirm the analytical properties of the modal derivative reduction and show the potential of the proposed novel complexity reduction methods, along with the current limitations. Copyright © 2016 John Wiley & Sons, Ltd.     

Fixed-precision randomized low-rank approximation methods for nonlinear model order reduction of large systems

Many model order reduction (MOR) methods employ a reduced basis to approximate the state variables. For nonlinear models, V is often computed using the snapshot method. The associated low-rank approximation of the snapshot matrix can become very costly as m,n grow larger. Widely used conventional singular value decomposition methods have an asymptotic time complexity of , which often makes them impractical for the reduction of large models with many snapshots. Different methods have been suggested to mitigate this problem, including iterative and incremental approaches. More recently, the use of fast and accurate randomized methods was proposed. However, most work so far has focused on fixed-rank approximations, where rank k is assumed to be known a priori. In case of nonlinear MOR, stating a bound on the precision is usually more appropriate. We extend existing research on randomized fixed-precision algorithms and propose a new heuristic for accelerating reduced basis computation by predicting the rank. Theoretical analysis and numerical results show a good performance of the new algorithms, which can be used for computing a reduced basis from large snapshot matrices, up to a given precision ε.     

合成孔径声纳干涉相位图噪声抑制方法分析

讨论了合成孔径声纳的干涉相位图降噪问题。利用加性噪声模型,分析了四种降噪算法:像素平均法、非线性条件邻域均值法、模糊中值法和向量滤波法。通过仿真数据生成的干涉相位图进行降噪分析得到结论:通过像素平均法和条件邻域均值法降噪后不能准确地生成高度图,不推荐在实际中使用。而模糊中值法和向量滤波法通过降噪可以准确地生成高度图。通过相位图残留点数和高度图方差对比发现,向量滤波法性能最优,是一种快速有效的噪声抑制方法。     

Dopplerlet变换推广及其在主动声纳中的应用

针对水下高速运动目标的主动探测问题,引入了Dopplerlet变换。根据运动目标回波的特点提出了主动Dopplerlet原子．并分析了基于主动Dopplerlet变换的自适应匹配塔形分解算法,克服了Dopplerlet变换在被动信号处理中声源频率变化引起误差增大的问题。将算法应用于高斯白噪声和高斯色噪声背景中运动目标的检测和速度距离的估计．取得了较好的结果。