混沌背景下信号的盲分离

混沌信号与确定性小信号叠加生成的混合信号是一更高维的混沌信号,因而不能用一般的混沌信号噪声抑制的方法进行分离.提出了一种这类信号盲分离的方法.在重构未知的混沌信号的动力方程时,充分利用混沌吸引子的几何特性,并且限定动力映射为原混沌吸引子所在流形的内部映射,从而保证了重构的动力系统方程对应于原混沌信号,而不是同样具有混沌特性的混合信号.然后利用重构的动力方程,借用混沌信号中的噪声抑制思想,估计出原混沌信号对应的轨道,实现信号分离.通过对Lorenz系统中谐波信号、Henon映象中自回归过程,以及脑电信号中谐波信号进行提取的数值实验,验证了信号盲分离方法的有效性和可行性. 关键词： 混沌 非线性 信号处理 盲分离     

Complex-wave retrieval based on blind signal separation

In the process of the reconstruction of digital holography, the traditional methods of diffraction and filtration are commonly adopted to recover the original complex-wave signal. Influenced by twin-image and zero-order terms, the above-mentioned methods, however, either limit the field of vision or result in the loss of the amplitude and phase. A new method for complex-wave retrieval is presented, which is based on blind signal separation. Three frames of holograms are captured by a charge coupled device (CCD)camera to form an observation signal. The term containing only amplitude and phase of complex-wave is separated, by means of independent component analysis, from the observation signal, which effectively eliminates the zero-order term. Finally, the complex-wave retrieval of pure phase wavefront is achieved.Experimental results show that this method can better recover the amplitude and phase of the original complex-wave even when there is a frequency spectrum mixture in the hologram.     

Complex-wave retrieval based on blind signal separation

In the process of the reconstruction of digital holography, the traditional methods of diffraction and filtration are commonly adopted to recover the original complex-wave signal. Influenced by twin-image and zero-order terms, the above-mentioned methods, however, either limit the field of vision or result in the loss of the amplitude and phase. A new method for complex-wave retrieval is presented, which is based on blind signal separation. Three frames of holograms are captured by a charge coupled device (CCD) camera to form an observation signal. The term containing only amplitude and phase of complex-wave is separated, by means of independent component analysis, from the observation signal, which effectively eliminates the zero-order term. Finally, the complex-wave retrieval of pure phase wavefront is achieved. Experimental results show that this method can better recover the amplitude and phase of the original complex-wave even when there is a frequency spectrum mixture in the hologram.     

信号盲分离的非线性累加和算法

就一种基于非线性累加和的信号盲分离方法作了探讨。首先,文中针对方法中非线性函数特性对信号分离的影响从理论上作了分析,给出了加快迭代收敛的约束条件。接着,将该方法从无卷积混合信号分离的情况推广到混合卷积情形。最后通过数值模拟,给出了信号分离结果。     

一种适用于微弱信号提取的盲源分离算法

本文首次提出了适用于微弱信号提取的盲源分离算法,这种方法是在常用的自然梯度串行更新算法基础上采用了有监督的机制。通过观察两个信号互相关的程度自动判别激活函数的种类,实现了超高斯亚高斯强弱信号混合下的盲源分离。信号仿真表明,有监督的盲源分离技术收敛速度较快,精度较高。     

海豚声纳信号的脉冲分解及特征分析

提出一种海豚声纳目标探测脉冲串信号的脉冲分解方法,在此基础上对海豚声纳脉冲信号的特征进行了分析,其中包括脉冲宽度、脉冲间隔以及脉冲的时间分辨率和频率分辨率,并采用耳蜗滤波对海豚声纳脉冲串信号的时频特征进行了分析。分析结果表明,探测目标的过程中,根据目标距离的不同,海豚会自适应调节脉冲信号的发射频率和信号形式。     

基于盲分离技术的水下多源环形阵定位方法研究

根据水下自动探测的工程实际技术发展需求,本文基于盲分离技术研究了环形阵对水下目标辐射噪声信号进行定位的方法,利用水声信号统计特性,通过构建针对水中目标的非线性函数和学习因子等,对自然梯度算法进行了改进,实船信号的盲分离实验结果表明,该方法改进了分离效果。在此基础上,建立了基于线性瞬时混合情况下的定位模型,完成了对六个水中目标的定位研究,取得了令人满意的结果。     

Effect of signal phase and masker separation on two-tone masking

The detectability of a sinusoid masked by two sinusoids was studied as a function of signal phase and the frequency separation between the two maskers. The signal frequency fs was equal to the arithmetic mean of the two masker frequencies, fl and fh, where fl less than fh. Signal frequencies of 1 and 4 kHz, eight signal phases, and 12 values of r = (fh-fl)/fs from 0.01-1.0 were used. The data could be divided into three regions. For large masker separations, r greater than 0.4, no consistent effects of signal phase were observed. For r less than 0.4, an effect of signal phase was evident at both signal frequencies. However, the effect of signal phase was different for the two regions 0.03 less than r less than 0.4 and r less than 0.03. For moderate masker separations, 0.03 less than r less than 0.4, masked thresholds were lowest at phases of 0 degrees and 180 degrees and highest at phases of 90 degrees and 270 degrees. For small masker separations, r less than 0.03, masked threshold was highest at 0 degree and the effect of signal phase depended on signal frequency. The different form of the phase effect for these three regions is discussed in terms of the use of different cues, arising from temporal resolution, spectral filtering, combination tones, and envelope spectra.     

Determination of plate source, detector separation from one signal

We address the problem of locating a transient source, such as an acoustic emission source, in a plate. We apply time-frequency analysis to the signals detected at a receiver. These highly dispersive and complex waveforms are measured for source-receiver separations ranging from 40 to 180 plate thicknesses and at frequencies such that 10 to 20 Rayleigh-Lamb branches are included. Reassigned, smoothed, pseudo-Wigner-Ville distributions are generated that exhibit the expected sharp ridges in the time-frequency plane, lying along the predicted frequency-time-of-arrival relations. The source-receiver separation can be determined from such plots.     

Chapter 7 Electronic systems for Doppler signal separation and processing

Chapter 7 Electronic systems for Doppler signal separation and processing     

Gradient method for blind chaotic signal separation based on proliferation exponent

A new method to perform blind separation of chaotic signals is articulated in this paper, which takes advantage of the underlying features in the phase space for identifying various chaotic sources. Without incorporating any prior information about the source equations, the proposed algorithm can not only separate the mixed signals in just a few iterations, but also outperforms the fast independent component analysis(FastICA) method when noise contamination is considerable.     

FCM-FastICA的点蚀声发射信号分离识别方法*

金属点蚀是一种破坏性和隐患较大的设备损伤形式。点蚀会产生声发射信号。点蚀过程中产生的多种声源类型会造成信号混叠,影响腐蚀进程的判断。针对点蚀信号混叠问题,提出一种模糊C均值聚类与快速独立分量分析算法相结合的点蚀信号分离识别方法。通过分析单、双点蚀声发射数据将点蚀分为钝化膜破裂阶段、点蚀诱导成核及发展阶段,由聚类确定信号类别并用快速独立分量分析分离混合信号,利用相关性函数验证分离效果。结果表明：单点蚀过程存在3类原信号,双点蚀过程存在7类信号,其中包含单个信号与混合信号;单个信号与原信号相关性极高,达到0.8以上,混合信号的分离分量与原信号相关性达到0.6以上,分离效果较好。该方法可对点蚀混合信号进行有效分离和识别,为腐蚀进程判断提供支持。     

基于改进自适应分解法的单通道雷达引信混合信号分离

为了更好地改善单通道多分量调频体制雷达引信信号的时频分布,以便于完成参数估计和信号分离,将自适应chirplet分解法引入到单通道雷达引信信号的分离问题当中,并加入时频曲线弯曲度因子,提出一种改进的自适应chirplet分解法,使其能够被用于非线性相位信号的分解,利用快速算法和优化算法,优化了运算过程,可准确且快速地求解出各参数,得到无交叉项的多分量信号时频分布,使复杂混合情况下的多分量信号也能够被较好地分离.通过仿真,利用该方法对多种情况下混合信号的时频分布进行了很好的改进,而后分解出了各分量信号,并通过分析计算出了相应的混合系数,仿真结果说明了该方法的有效性,且具有一定的抗噪声性能.     

Raman and photoluminescence signal separation in Raman hyperspectral imagery including noise reduction

Raman hyperspectral imaging (RHSI) is a valuable tool for gaining crucial information about the chemical composition of materials. However, obtaining clear Raman signals is not always a trivial task. Raw Raman signals can be susceptible to photoluminescence interference and noise. Hence, the preprocessing of RHSI is a required step for an effective and reliable chemical analysis. The main challenge is splitting the measured RHSI into separate Raman photoluminescence signals. Since no golden-standard exists, it is non-trivial to validate the correctness of the separated signals. While current state-of-the-art preprocessing methods are effective, they require expert knowledge and involve unintuitive hyperparameters. Current approaches also lack generalizability, requiring extensive hyperparameter tuning on a case-by-case basis, while even then results are not always as expected. To this end, this work proposes a novel iterative RHSI preprocessing pipeline for splitting raw Raman signals and noise removal based on linear spline and radial basis function regression (IlsaRBF). The proposed method involves hyperparameters based on the physical properties of Raman spectroscopy, making them intuitive to use. This leads to more robust and stable hyperparameters, reducing the necessity for extensive hyperparameter tuning. A thorough evaluation shows that the proposed method outperforms the current state-of-the-art. Additionally, a cosmic ray identification and removal algorithm (CRIR) and dynamic PCA for noise reduction are introduced. A standalone tool containing our proposed methods is provided, making RHSI preprocessing available to a broader audience, aiding further research and advancements in the field of Raman spectroscopy.     

A new blind fault component separation algorithm for a single-channel mechanical signal mixture

A vibration signal collected from a complex machine consists of multiple vibration components, which are system responses excited by several sources. This paper reports a new blind component separation (BCS) method for extracting different mechanical fault features. By applying the proposed method, a single-channel mixed signal can be decomposed into two parts: the periodic and transient subsets. The periodic subset is related to the imbalance, misalignment and eccentricity of a machine. The transient subset refers to abnormal impulsive phenomena, such as those caused by localized bearing faults. The proposed method includes two individual strategies to deal with these different characteristics. The first extracts the sub-Gaussian periodic signal by minimizing the kurtosis of the equalized signals. The second detects the super-Gaussian transient signal by minimizing the smoothness index of the equalized signals. Here, the equalized signals are derived by an eigenvector algorithm that is a successful solution to the blind equalization problem. To reduce the computing time needed to select the equalizer length, a simple optimization method is introduced to minimize the kurtosis and smoothness index, respectively. Finally, simulated multiple-fault signals and a real multiple-fault signal collected from an industrial machine are used to validate the proposed method. The results show that the proposed method is able to effectively decompose the multiple-fault vibration mixture into periodic components and random non-stationary transient components. In addition, the equalizer length can be intelligently determined using the proposed method.     

Suppression of sampling artefacts in high-resolution four-dimensional NMR spectra using signal separation algorithm

The development of non-uniform sampling (NUS) strategies permits to obtain high-dimensional spectra with increased resolution in significantly reduced experimental time. We extended a previously proposed signal separation algorithm (SSA) to process sparse four-dimensional NMR data. It is employed for two experiments carried out for a partially unstructured 114-residue construct of chicken Engrailed 2 protein, namely 4D HCCH-TOCSY and 4D C,N-edited NOESY. The SSA allowed us to obtain high-quality spectra using only as little as 0.16% of the available samples, with low sampling artefacts approaching the thermal noise level in most spectral regions. It is demonstrated that NUS 4D HCCH-TOCSY is dominated by sampling noise and requires efficient artefact suppression. On the other hand, 4D C,N-edited NOESY is a particularly attractive experiment for NUS, as the absence of diagonal peaks renders the problem of artefacts less critical. We also present a transverse-relaxation optimized sequence for HMQC that is especially designed for longer evolution periods in the indirectly detected proton dimension in high-dimensional pulse sequences. In conjunction with novel sampling strategies and efficient processing methods, this improvement enabled us to obtain unique structural information about aliphatic-amide contacts.     

稀疏正交联合约束多通道非负矩阵分解声信号分离算法

针对复杂环境下多通道声信号分离问题,提出稀疏正交联合约束多通道非负矩阵分解声信号分离方法。首先设计基于多通道扩展坂仓斋藤(Itakura-Saito,IS)散度的稀疏正交联合约束项构造代价函数,给出信号稀疏和信号正交约束辅助函数,实现代价函数最小化求解。然后通过迭代更新规则设计,得到稀疏正交优化的多通道非负矩阵分解基矩阵和系数矩阵,讨论了稀疏正交约束对基矩阵和系数矩阵稀疏性与连续性影响。最后基于多通道信号空间特性,进行了非负矩阵分解基聚类以获得多通道非负矩阵分解声信号的分离结果。双通道音频数据与四通道声学目标分离实验数据测试表明,对音频数据,所提算法在性能指标信号失真比(SDR)上提高了0.84dB,对于直升机声源数据,所提算法在SDR上提高了4.53dB。