特征系统实现算法中的噪声问题研究

研究特征系统实现算法在模态识别过程中的噪声处理问题.特征系统实现算法为目前土木工程领域应用较为广泛的一种模态识别法.在处理实测数据时,特征系统实现算法将实测数据分解为结构真实信号和噪声信号,从而将噪声消除.分块Hankel矩阵的维数对信噪分离过程影响很大.提出当分块Hankel矩阵的分块行与分块列数接近时,真实和噪声信号可以最好的分离.通过在实测信号里添加噪声(产生噪声-噪声信号),将实测信号和噪声—噪声信号识别的结果进行比较,提出了1种验证识别模态参数是否为结构真实模态的检验方法.本文应用导管架平台实例来验证提出方法的适用性.结果表明,当分块Hankel矩阵的分块行与分块列数接近时,真实和噪声信号能够有效地分离,此时添加的噪声主要影响噪声信号部分.由实测信号和噪声噪声信号识别的模态参数非常吻合.     

复指数方法降噪技术及其试验研究

实测信号中的噪声,以及模型阶次的不确定性给模态参数的准确识别带来困难.以提高模态参数识别精度为目标,提出基于模30-定阶和信噪分离的复指数模态参数识别方法.该方法借助奇异值分解技术确定模型阶次,采用结构低秩逼近方法进行信噪分离.在此基础上.利用复指数法进行模态参数识别.分别选取一维的悬臂梁模型和二维的悬挂板模型进行物理模型实验,结果表明:该方法提高模态参数的识别梢度,尤其是阻尼比的识别精度,具有较好的工程应用前景.     

海洋平台结构模态参数识别的仿真研究

将特征系统实现法(ERA)与CBSI算法相结合识别海洋平台结构的模态参数(固有频率,阻尼比,振型),利用有限的加速度信号进行结构模态参数识别。海洋平台结构数值模型模拟的动力响应信号用于参数识别。所识别出的海洋平台模态参数与有限元ANSYS分析所得的结果相比较,表明运用本文所提出的方法进行模态参数识别是有效的。     

复指数方法降噪技术及其试验研究

实测信号中的噪声,以及模型阶次的不确定性给模态参数的准确识别带来困难。以提高模态参数识别精度为目标,提出基于模型定阶和信噪分离的复指数模态参数识别方法。该方法借助奇异值分解技术确定模型阶次,采用结构低秩逼近方法进行信噪分离。在此基础上,利用复指数法进行模态参数识别。分别选取一维的悬臂梁模型和二维的悬挂板模型进行物理模型实验,结果表明:该方法提高模态参数的识别精度,尤其是阻尼比的识别精度,具有较好的工程应用前景。     

一种模态参数识别的虚假模态剔除技术

针对背景噪声下结构的模态参数识别结果存在虚假模态的情况,分析虚假模态产生的原因,通过理论推导提出辨别虚假模态的判断条件,根据判断条件剔除识别模态参数中包含的虚假模态。以比例阻尼海洋平台结构数值模型为例,在模拟噪声水平5%的工况下,获得结构脉冲响应信号,对基于奇异值分解定阶消噪后的信号用复指数法进行模态参数识别,对识别的模态频率和阻尼比进行虚假模态剔除。结果表明:根据判断条件可有效剔除所识别参数中的虚假模态。     

高频地波雷达海杂波抑制的时频-矩阵联合法

高频地波雷达海面回波信号中含有很强的海杂波分量,造成虚警与漏警现象,严重影响了对船只目标的探测与管理。本文提出基于时频分析与矩阵分解相联合的海杂波抑制方法。该方法从时频域中检测出海杂波的时变频率范围,同时通过Hankel降秩得到各回波分量奇异值及瞬时频率估计;通过频率匹配及矩阵重构给出海杂波的时域分量,予以剔除。实测雷达数据处理结果表明,在保留目标时域回波与频谱特征的情况下,本方法成功实现了海杂波分量的分离与抑制,提高了对海面目标的探测性能。     

基于实测数据的海上风电结构动力响应分析

地震是危害海上风电结构作业安全的重要环境因素,目前,国内尚未公开发表真实地震响应下,海上风电结构的实测动力响应数据。分析了某地震活动区海上风电结构的实测地震响应,采用随机子空间识别方法进行风机的模态识别,阐述了风机机舱偏航将引起前后、左右两个正交方向振动的耦合,并从理论上证明了利用耦合、解耦数据识别模态参数的差异。结果表明：1）耦合与解耦信号识别的频率、阻尼比完全相同,而耦合信号识别的模态振型与偏航角有关;2）地震作用会对结构产生巨大冲击;3）非地震作用下,风机塔筒前后、左右第一阶弯曲模态为主要模态,地震作用可以激发风机的高阶模态,使得塔筒中上部而不是顶部的振动响应最大。此分析对地震活动区海上风电结构的抗震设计具有一定的参考价值。     

带乘性噪声系统的最优方差约束鲁棒状态估计算法

带乘性噪声系统由于其广泛的适用性,一直成为研究的热点。针对带乘性噪声系统的鲁棒状态估计算法进行研究,利用线性矩阵不等式的方法,讨论状态方程中含有范数有界不确定性参数的带乘性噪声系统的方差约束鲁棒状态估计器存在的条件,并针对此类带乘性噪声系统推导出1套方差约束鲁棒状态估计算法以及最优方差约束鲁棒状态估计算法。仿真结果验证算法的有效性。     

基于弹性网回归的水下目标无监督特征选择算法

针对水声数据的特征中含有大量冗余、不相关和噪声特征,导致水声目标识别正确率降低的问题,提出了一种新的水下目标特征选择方法——基于弹性网回归的无监督特征选择算法(Unsupervised fea- ture selection algorithm based on elastic-net regression,UFSER)。 该算法利用谱回归得到高维水声数据和其低维表示之间的回归系数矩阵,并且在回归框架中加入弹性网惩罚项优化求解回归系数矩阵;最后,对回归系数矩阵进行稀疏化从而对特征的分类性能进行评价。 使用实测水声数据集和 UCI 声呐数据集进行特征选择和 SVM 分类实验。 实验结果表明：在特征数目分别减少 60.6%和 60%的情况下,分类识别正确率较特征选择前提升了 1.05%和 6.6%。     

带乘性噪声系统的极大似然最优估计算法

对带乘性噪声系统（ＳＭＮ）在乘性噪声及其统计参数未知的情况下,基于极大似然准则,提出了一种分块组合优化估计算法（ＢＣＯＥＡ）。该算法不需要事先知道乘性噪声的统计参数,可同时进行状态最优估计以及乘性噪声序列和其统计参数的最优估计。     

Note on axisymmetric consolidation of multi-layered poroelastic soils with compressible constituents

In the conventional analysis of Biot’s axisymmetric consolidation, the solid phase and the surrounding fluid were often assumed incompressible for simplicity. Such assumption in soil engineering ignored the effect of compressibility of constituents on the consolidation. In this article, the compressible fluid and solid in soil were taken into account for 3-D consolidation. The pore pressure, the displacements, and the stresses were expressed by two displacement functions, and the Laplace–Hankel transform was applied to set up the stiffness matrix between the generalized displacement and stress. The stiffness matrix consisted of negative exponential functions, ensuring that the computation is efficient and stable. Then the global stiffness matrix is extended by embracing the continuity of the interfaces and boundary conditions of soil base. The relationship between the generalized displacement and stress of the soil base reduces the number of unknowns of the global matrix. Such consideration reduced the number of unknowns of the global matrix and brought in more acceptable boundary conditions where the stiffness and the permeability of semi-infinite soil base can be taken into account. After the inversion of the Laplace–Hankel transform, the real solutions were obtained. The results show that the stiffness and permeability of the soil base can change the development consolidation and that the compressibility of solid particles has a great effect on the settlement in the beginning of consolidation.     

Super-resolution processing for HF surface wave radar based onpre-whitened MUSIC

Owing to the decametric wavelength, the angular resolution of high-frequency surface wave radar (HFSWR) is usually coarse, especially when dimensions of antenna arrays are restricted such as in shipborne HFSWR applications. In this paper, the relative strength of atmospheric noise and sea clutter that will heavily degrade the capabilities of HFSWR in target detection and resolution are calculated, then a method for estimating the spatial covariance matrix of background noise is presented. By introducing a pre-whitening procedure in multiple signal classification (MUSIC), the resolution performance of MUSIC is enhanced in spatially colored noise environment. Results with data from the aircraft detecting experiment conducted by the Research Institute of Electronic Engineering of Harbin Institute of Technology in 1994 and simulated data of two targets show that pre-whitened MUSIC can provide a better resolution and accurate determination of target number. Furthermore, a post-processing method is proposed to eliminate the sidelobe of spatial spectrum arising from the estimation errors of a noise covariance matrix     

自噪声有源抵消器性能分析

降低测量平台处本艇自噪声能够有效地提高被动声纳设备作用距离。文章分析了参考输入中含有目标信号的本艇自噪声有源抵消器的性能。通过理论分析可以看出，随着本艇自噪声有源抵消器参考输入端信噪比的降低，系统输出端信噪比升高。同时。计算机仿真说明，只要参考输入中信号分量较少，自噪声有源抵消器仍然可以达到较好的降噪效果。     

A Study of Spatial Processing Gain in Underwater Acoustic Communications

Spatial processing, including beamforming and diversity combining, is widely used in communications to mitigate intersymbol interference (ISI) and signal fading caused by multipath propagation. Beamforming suppresses ISI (and noise) by eliminating multipath (and noise) arrivals outside the signal beam. Beamforming requires the signals to be highly coherent between the receivers. Diversity combining combats ISI as well as signal fading by taking advantage of the independent information in the signal. Classical (spatial) diversity requires that signals are independently fading, hence are (spatially) uncorrelated with each other. In the real world, the received signals are neither totally coherent nor totally uncorrelated. The available diversity is complex and not well understood. In this paper, we study the spatial processing gain (SPG) as a function of the number of receivers used, receiver separation, and array aperture based on experimental data, using beamforming and multichannel combining algorithms. We find that the output symbol signal-to-noise ratio (SNR) for a multichannel equalizer is predominantly determined by the array aperture divided by the signal coherence length, with a negligible dependence on the number of receivers used. For a given number of receivers, an optimal output symbol SNR (OSNR) is achieved by spacing the receivers equal to or greater than the signal coherence length. We model the SPG in decibels as the sum of the noise suppression gain (NSG, equivalent to signal-to-noise enhancement) and the ISI suppression gain (ISG, equivalent to signal-to-ISI enhancement) both expressed in decibels; the latter exploits the spatial diversity and forms the basis for the diversity gain. Data are interpreted using the modeled result as a guide. We discuss a beam-domain processor for sonar arrays, which yields an improved performance at low-input SNR compared to the element-domain processor because of the SNR enhancement from beamforming many sensors.     

Extension of the Frequency-Domain pFFT Method for Wave Structure Interaction in Finite Depth

To analyze wave interaction with a large scale body in the frequency domain, a precorrected Fast Fourier Transform (pFFT) method has been proposed for infinite depth problems with the deep water Green function, as it can form a matrix with Toeplitz and Hankel properties. In this paper, a method is proposed to decompose the finite depth Green function into two terms, which can form matrices with the Toeplitz and a Hankel properties respectively. Then, a pFFT method for finite depth problems is developed. Based on the pFFT method, a numerical code pFFT-HOBEM is developed with the discretization of high order elements. The model is validated, and examinations on the computing efficiency and memory requirement of the new method have also been carried out. It shows that the new method has the same advantages as that for infinite depth.     

The characteristics of signal versus noise sst variability in the north pacific and the tropical pacific ocean

Total sea surface temperature (SST) in a coupled GCM is diagnosed by separating the variability into signal variance and noise variance. The signal and the noise is calculated from multi-decadal simulations from the COLA anomaly coupled GCM and the interactive ensemble model by assuming both simulations have a similar signal variance. The interactive ensemble model is a new coupling strategy that is designed to increase signal to noise ratio by using an ensemble of atmospheric realizations coupled to a single ocean model. The procedure for separating the signal and the noise variability presented here does not rely on any ad hoc temporal or spatial filter. Based on these simulations, we find that the signal versus the noise of SST variability in the North Pacific is significantly different from that in the equatorial Pacific. The noise SST variability explains the majority of the total variability in the North Pacific, whereas the signal dominates in the deep tropics. It is also found that the spatial characteristics of the signal and the noise are also distinct in the North Pacific and equatorial Pacific.     

顶点成分分析算法在端元数目估计中的应用研究

确定端元数目是混合像元分解中端元提取的前提,目前端元数目主要还是通过图像处理人员目视判读来获取。 在研究顶点成分分析算法的原理和端元光谱之间在大多数情况下相互独立的基础上,提出一种基于顶点成分分析算法的端元数目估计方法。 通过在不同端元数目、对噪声的稳健性、图像大小不同时的实验表明,该方法能够比较正确、有效地估计图像中的端元数目,文中方法与其他端元数目估计算法及相关系数阈值的自动化确定问题还需要进一步研究。     

"Principal component inverse" algorithm for detection in thepresence of reverberation

Detection in the presence of reverberation is often difficult in active sonar, due to the reflection/diffusion/diffraction of the transmitted signal by the ocean surface, ground, and volume. A modelization of reverberation is often used to improve detection because classical algorithms are inefficient. A commonly used reverberation model is colored and nonstationary noise. This model leads to elaborate detection algorithms which normalize and whiten reverberation. In this paper, we focus on a more deterministic model which considers reverberation as a sum of echoes issued from the transmitted signal. The Principal Component Inverse (PCI) algorithm is used with this model to estimate and delete the reverberation echoes. A rank analysis of the observation matrix shows that PCI is efficient in this configuration under some conditions, such as when the transmitted signal is Frequency Modulated. Both methods are validated with real sonar surface reverberation noise. We show that whitening has poor performance when reverberation and target echo have the same properties, while PCI maintains the same performance whatever the reverberation characteristics. Further, we extend the algorithms to spatio-temporal data. We propose a new algorithm for PCI which allows better echo separation. This new method is shown to be more efficient on real spatio-temporal data     

多极化SAR溢油探测影响因素分析研究

Polarimetric synthetic aperture radar(SAR) oil spill detection parameters conformity coefficient(μ), Muller matrix parameters(|C|, B_0), the eigenvalues of simplified coherency matrix(λ_(nos)) and the influence of SAR observing parameters, ocean environment and noise level are investigated. Radarsat-2 data are used to make systematic analysis of polarimetric parameters for different incidences, wind speeds, noise levels and the ocean phenomena(oil slick and look likes). The influence of the SAR observing parameters, the ocean environment and the noise level on the typical polarimetric SAR parameter conformity coefficient has been analyzed. The results indicate that conformity coefficient cannot be simply used for oil spill detection, which represents the image signal to the noise level to some extent. When the signals are below the noise level for the oil slick and the look likes, the conformity coefficients are negative; while the signals above the noise level corresponds to positive conformity coefficients. For dark patches(low wind and biogenic slick) with the signal below the noise,polarization features such as conformity coefficient cannot separate them with oil slick. For the signal above the noise, the oil slick, the look likes(low wind and biogenic slick) and clean sea all have positive conformity coefficients, among which, the oil slick has the smallest conformity coefficient, the look likes the second, and the clean sea the largest value. For polarimetric SAR data oil spill detection, the noise plays a significant role. So the polarimetric SAR data oil spill detection should be carried out on the basis of noise consideration.     

A New Method for Matched Field Localization Based on Two-Hydrophone

The conventional matched field processing (MFP) uses large vertical arrays to locate an underwater acoustic target. However, the use of large vertical arrays increases equipment and computational cost, and causes some problems such as element failures, and array tilting to degrade the localization performance. In this paper, the matched field localization method using two-hydrophone is proposed for underwater acoustic pulse signals with an unknown emitted signal waveform. Using the received signal of hydrophones and the ocean channel pulse response which can be calculated from an acoustic propagation model, the spectral matrix of the emitted signal for different source locations can be estimated by employing the method of frequency domain least squares. The resulting spectral matrix of the emitted signal for every grid region is then multiplied by the ocean channel frequency response matrix to generate the spectral matrix of replica signal. Finally, the matched field localization using two-hydrophone for underwater acoustic pulse signals of an unknown emitted signal waveform can be estimated by comparing the difference between the spectral matrixes of the received signal and the replica signal. The simulated results from a shallow water environment for broadband signals demonstrate the significant localization performance of the proposed method. In addition, the localization accuracy in five different cases are analyzed by the simulation trial, and the results show that the proposed method has a sharp peak and low sidelobes, overcoming the problem of high sidelobes in the conventional MFP due to lack of the number of elements.