船舶航行辐射噪声信号仿真方法研究

船舶航行辐射噪声是被动声纳探测、声纳目标识别、听音判型等工作的重要信息源,其信号仿真是解决上述研究工作中噪声样本类型少或数量不足的有效方法,前提是所仿真信号必须满足逼真度要求。提出了一种基于实测信号重构的船舶航行辐射噪声信号仿真方法。通过谱估计提取信号的连续谱、线谱、调制谱特征,根据谱估计数据合成满足信号谱特征要求的船舶航行辐射噪声,并通过听觉感受调整调制谱参数。仿真结果显示所合成船舶航行辐射噪声信号能够从谱估计和听觉感受两方面逼近实测辐射噪声信号。      

船舶辐射噪声信号相似度评估方法研究

船舶辐射噪声的信号相似度评估结果,可为噪声信号来源判定、仿真信号置信度评价等应用提供一种判断依据。提出一种船舶辐射噪声的信号相似度评估方法,综合了船舶辐射噪声信号的听觉、连续谱、线谱和调制谱特征,针对每一特征选择了具体指标参数和特征相似度的计算方法,然后在各特征相似度基础上实现船舶辐射噪声信号之间总体相似度的计算。利用实测船舶辐射噪声信号进行了数据试验,并给出了详细的评估和数据分析过程。试验结果表明,采用该方法得出的相似度评估数据,与船舶辐射噪声信号的实际相似情况一致,且数值区间的可分性好,因而具有较高的可信度。      

船舶航行辐射噪声的多普勒效应仿真

通过分析、比较已有水声信号多普勒频移仿真方法，针对船舶航行辐射噪声提出一种能够适应声源和接收者相对径向速度变化的、计算效率较高的水下宽带噪声信号多普勒效应仿真方法。仿真实验结果显示，该方法使船舶航行辐射噪声信号频域各分量的频率随着相对径向速度的变化呈现出不同的多普勒频移量，使得船舶航行辐射噪声信号的仿真达到了更高的逼真度。     

船舶噪声合成与听觉感知分析

船舶噪声人耳听觉感知训练是提高声呐员目标识别能力的重要手段,但是实录各类船舶在各个状态下的辐射噪声作为训练噪声样本根本无法实现,而且实录船舶辐射噪声易受环境噪声和海洋声信道传播的影响,无法清晰且不失真地呈现船舶的状态信息。依据人耳听觉感知的临界带通特性,提出了通过临界带通滤波和线谱迭加合成方法合成船舶辐射噪声的方法,并对舰船辐射噪声的原始声和合成声进行了听觉感知特性分析。研究结果表明辐射噪声的合成声与原始声在尖锐度、粗糙度和波动强度三个音色属性方面一致,可以作为船舶噪声人耳听觉感知训练中的噪声样本。     

船舶高压电气设备局部放电模式快速识别研究

船舶高压电气设备局部放电严重影响电气设备的正常运行与船舶安全,但是不同类型的局部放电在放电过程产生的超高频电磁波信号存在显著差异,需要根据其电磁波信号进行模式识别,而传统的检测方法无法适应船舶强电磁干扰、高频机械振动噪声的恶劣环境。为此,本文设计了可以快速进行船舶高压电气设备局部放电模式快速识别的方法。该方法根据船舶高压电气设备超高频电磁波信号的特点,提取四种典型局放信号以及待测信号的特征值向量,通过计算模式识别可信度因子R,实现对待测局放信号的快速准确识别,试验测试表明该方法能够快速识别船舶高压设备局部放电类型,提升了船舶高压设备的运行可靠性。     

船舶航行辐射噪声的主动控制方法研究

船舶航行辐射噪声控制对于环境保护和军事应用均具有重要意义。根据船舶的航行轨迹和接收点建立了自适应噪声抵消模型,从船舶外部对其产生的特定低频线谱进行主动控制,实现局部自由声场静区。针对低频线谱信号,在船舶上布设自适应次级声源,进行了不同信号频率的仿真,研究其在特定空间内产生的降噪效果。仿真显示当船舶在特定区域内航行时,次级声源的开启能够在接收点处实现特定低频线谱信号的有效抵消。针对仿真结果,理论推导了有效声抵消区域半径的计算公式。由此得到了一种可能的船舶航行辐射噪声低频线谱主动控制方法:首先计算有效声抵消区域半径,当船舶进入或离开该区域时,动态开启或关闭次级声源,从而实现噪声的有源控制。      

船舶实测辐射噪声信号的调制谱重构仿真

提出一种船舶实测辐射噪声信号的调制谱重构仿真方法.对实测信号进行时域波形和调制谱分析,提取螺旋桨旋转周期、叶片数、脉冲宽度、调制深度参数;通过仿真生成调制脉冲幅度参数与调制谱谐波谱线之间关系的对照表,查表确定各螺旋桨叶片的调制脉冲幅度参数;基于上述参数实现船舶实测辐射噪声信号的调制谱重构.仿真结果表明,该重构方法能够较好地保持调制谱第一组特征谐波谱线的频率、谱级相对大小排序等重要信号特征.     

多噪声背景下船舶轴频电场信号的提取

船舶轴频电场信号是船舶的固有特征之一.浅海船舶轴频电场测量信号受到了低频周期噪声和海洋环境电场噪声等噪声的多重干扰,使得信号的提纯存在困难.这里具有针对性地对浅海船舶轴频电场信号进行分析,先通过时频分析方法对所得信号进行预处理,最后利用基于LMS算法的自适应滤波方法对测量信号进行噪声抵消.结果证明,这种方法确实使得干扰噪声得到大幅度抵消.     

船舶辐射噪声无失真获取技术

船舶水下辐射噪声具有频带覆盖范围宽、幅值动态范围大等特点。为了准确地获取被测船舶的水下辐射噪声的频谱特征,需要在较宽的频带上研究船舶水下辐射噪声的水声阵列处理问题。本文主要研究船舶宽频带水下辐射噪声的水声阵列处理方法。首先研究基于嵌套直线阵的恒定束宽波束形成原理,建立仿真模型;其次,在获得的嵌套阵模型基础上,设计远场条件下的宽频带恒定束宽波束形成器;最后通过数据仿真验证文中所提方法的有效性。     

使用小波理论分析信号功率谱的二次处理方法

本论文研究讨论一种利用小波分析理论对功率谱进行二次处理的方法．论文首先介绍了这种方法的应用背景,给出了舰船辐射噪声功率的结构．在简明扼要地介绍了小波理论的有关基础知识,给出线谱的数学解释并分析了它在尺度分解中的特点之后,论文基于多尺度分析与奇异性信号检测的理论,对信号的功率谱做尺度分解,利用线谱的奇异性捕捉舰船辐射噪声功率谱中的线谱分量．由实船辐射噪声信号的分析结果我们看到,这种二次处理的方法不仅可以准确定位低信噪比的线谱,而且可以同时提取所有可能的线谱信号．     

稳定分布的水声信号处理新方法

本文依据分数低阶统计信号处理理论和舰船辐射噪声信号特性,提出了水声信号新的噪声模型,分析了常规LMS滤波方法在非高斯稳定分布噪声下性能退化的原因,提出了基于分数低阶p-范数的LMP,NLMP水声信号自适应降噪滤波方法,并分析了参数p值的选取对LMP滤波效果的影响.理论分析和计算机仿真结果表明这种方法在高斯和非高斯稳定分布条件下都具有良好的鲁棒性.     

Adaptive filtering of evoked potentials with radial-basis-function neural network prefilter

Evoked potentials (EPs) are time-varying signals typically buried in relatively large background noise. To extract the EP more effectively from noise, we had previously developed an approach using an adaptive signal enhancer (ASE) (Chen et al., 1995). ASE requires a proper reference input signal for its optimal performance. Ensemble- and moving window-averages were formerly used with good results. In this paper, we present a new method to provide even more effective reference inputs for the ASE. Specifically, a Gaussian radial basis function neural network (RBFNN) was used to preprocess raw EP signals before serving as the reference input. Since the RBFNN has built-in nonlinear activation functions that enable it to closely fit any function mapping, the output of RBFNN can effectively track the signal variations of EP. Results confirmed the superior performance of ASE with RBFNN over the previous method.     

移动通信信号自动调制识别的谱相关方法

一些不同的数字调制信号有着相同或相近的功率谱密度,但它们的谱相关函数却有明显区别。平稳的噪声和干扰的谱相关为零。利用谱相关函数的这些性质可以识别出噪声中的调制信号。本文提出了一种用于移动通信信号自动调制识别的谱相关算法,该方法可以识别各种主要无线系统和标准所采用的调制类型。     

Nonlinear blind source separation using a hybrid RBF-FMLP network

A novel scheme for blind source separation of nonlinearly mixed signals is developed using a hybrid system based on radial basis function (RBF) and feedforward multilayer perceptron (FMLP) networks. In this paper, the development of the proposed RBF-FMLP network is discussed, which hinges on the theory of nonlinear regularisation. The proposed network uses simultaneously local and global mapping bases to perform both signal separation and reconstruction of continuous signals in addition to signals that exhibit a high degree of fluctuation. The parameters of the proposed system are estimated jointly using the generalised gradient descent approach thereby rendering the training process relatively simple and efficient in computation. Simulations of both synthetic and speech signals have been undertaken to verify the efficacy of the proposed scheme in terms of speed, accuracy and robustness against noise.     

Instantaneous Frequency Estimation of Nonlinear Frequency-Modulated Signals Under Strong Noise Environment

Instantaneous frequency (IF) is the most important parameter of a signal, which is an important representation of non-stationary signals, such as frequency-modulated signals. Usually, signals are received with noises. Under noise environment, the conventional IF estimation methods for nonlinear frequency-modulated (NLFM) signal cannot work. In this paper, we focus on how to extract IF of NLFM signal under strong noise environment. First, a modified S-method (SM) is proposed to represent the time–frequency (TF) characteristic. The modified SM uses an adaptive smooth window. The symmetric window is used for multi-component signals and asymmetric window for mono-component signals. The modified SM enhances the TF energy concentration and suppresses the cross-terms effectively. Then, the Viterbi algorithm is used to extract the IF from the TF plane. Viterbi algorithm is a hidden Markov chain approach, which is proposed here as the IF estimator. The proposed method is utilized for various types of NLFM signals. Simulation results demonstrate the efficiency and validity of the proposed method under strong noise environment.     

Signal decomposition of transmembrane voltage-sensitive dye fluorescence using a multiresolution wavelet analysis

Fluorescence imaging of transmembrane voltage-sensitive dyes is used to study electrical activation in cardiac tissue. However, the fluorescence signals, typically, have low SNRs and may be contaminated with motion artifact. In this report, we introduce a new processing approach for fluoresced transmembrane potentials (fTmps) that is based upon a discrete wavelet transform. We show how fTmp signals can be decomposed and reconstructed to form three subsignals that contain signal noise (noise signal), the early depolarization phase of the action potential (rTmp signal), and motion artifact (rMA signal). A coiflet4 wavelet is used for fTmp decomposition and reconstruction of these subsignals. Results using fTmp signals that are contaminated with motion artifact indicate that the approach is a useful processing step to remove baseline drift, reduce noise, and reveal wavefronts. It streamlines the preprocessing of fTmps for the subsequent measurement of activation times and conduction velocities. It is a promising approach for studying wavefronts without aggressive mechanical tissue constraint or electromechanical uncoupling agents and is, useful for single-camera systems that do not provide for ratiometric imaging.     

Generic radial orthogonal moment invariants for invariant image recognition

As the variation of parameters in Jacobi polynomial, Jacobi–Fourier moments can form various types of orthogonal moments: Legendre–Fourier moments, Orthogonal Fourier–Mellin moments, Zernike moments, pseudo-Zernike moments, and so on. In this paper, we present a generic approach based on Jacobi–Fourier moments for scale and rotation invariant analysis of radial orthogonal moments, named Jacobi–Fourier moment invariants (JFMIs). It provides a fundamental mathematical tool for invariant analysis of the radial orthogonal moments since Jacobi–Fourier moments are the generic expressions of radial orthogonal moments. Theoretical and experimental results also show the superiority of the proposed method and its robustness to noise in comparison with some exist methods.