共查询到18条相似文献,搜索用时 93 毫秒
1.
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。 相似文献
2.
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. 相似文献
3.
4.
5.
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。 相似文献
6.
7.
8.
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。 相似文献
9.
10.
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。 相似文献
11.
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。 相似文献
12.
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... 相似文献
13.
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。 相似文献
14.
Todd Chapman Philip Avery Pat Collins Charbel Farhat 《International journal for numerical methods in engineering》2017,109(12):1623-1654
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. 相似文献
15.
Oliver Weeger Utz Wever Bernd Simeon 《International journal for numerical methods in engineering》2016,108(13):1579-1602
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. 相似文献
16.
C. Bach F. Duddeck L. Song 《International journal for numerical methods in engineering》2019,119(8):687-711
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. 相似文献
17.
18.
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。 相似文献