基于特征分析的自适应干扰抑制方法在水声目标被动测距中的应用

针对海洋环境中真实目标常常被强干扰掩盖而难以检测与定位的问题,本文提出了一种基于特征分析的自适应干扰抑制（EAAIS）方法,并将其应用于水声目标被动测距。EAAIS方法预先确定目标方向在一个可能的角度范围内,根据接收数据互谱密度矩阵（CSDM）单个特征向量的波束形成,构造合适的判决因子来自适应地估计并去除CSDM特征空间中的非目标信号子空间以抑制干扰和噪声。然后利用修正后的CSDM进一步获得更清晰的目标的声场干涉条纹,通过匹配声强的频谱结构进行水声目标被动测距。海试数据验证了EAAIS方法在强干扰环境下有效实现水声目标被动测距的能力。     

子空间判决分析的强干扰抑制

针对海洋中存在的强干扰和环境噪声导致水下目标方位估计算法性能剧烈下降的问题,提出了一种子空间判决分析的强干扰抑制方法 (SSJ),可实现多个强干扰下的目标方位估计。根据常规波束形成粗估的目标角度区间,利用目标-干扰-噪声子空间与导向矢量的相关性,设置判决项和估计合适的判决阈值来分离和抑制样本协方差矩阵中的非目标信息,降低干扰和噪声的输出功率,同时提高输出信干噪比,为增强阵列的目标方位分辨能力提供方法支撑。仿真和海试数据处理结果显示,SSJ方法可抑制目标角度区间外的强干扰和噪声,明显降低了干扰的输出功率和目标主瓣附近的旁瓣级,提高了目标方位角度的分辨力。相比于现有的子空间干扰抑制方法,所提方法具有更加稳健的干扰抑制能力。     

空时多普勒频移域运动小目标的抗干扰探测方法

有源声呐使用多普勒敏感信号可在多普勒频移域分离杂波、混响等干扰和运动目标,但低信干比时强干扰的时间旁瓣泄露和多普勒旁瓣泄露会淹没弱目标。针对该问题,提出了一种基于自适应最小均方(LMS)算法和宽带模糊函数(WAF)的运动小目标抗干扰探测方法。首先在空间多普勒频移域上利用基于LMS的自适应陷波器抑制干扰,然后在快时间多普勒频移域上利用WAF探测小目标。给出了理论推导和仿真分析,进行了水池试验。结果表明,该方法可有效抑制强干扰并能准确估计小目标的时延及多普勒频移,在水池试验中约有13 dB的干扰抑制能力。此外,该方法能够容忍归一化幅度起伏方差小于0.5的干扰起伏,提高了有源声呐对运动小目标的探测能力。     

稀疏矢量阵信号栅瓣及对称伪峰干扰抑制

稀疏矢量阵进行宽带信号处理时,强目标的栅瓣及对称伪峰会干扰弱目标的检测,提出了一种基于理论干扰预测的加权抑制方法。方法利用矢量阵波束形成先获得强目标的波达方向(Direction Of Arrival,DOA)信息以及目标所在方位各频率的幅度谱信息,再利用理论计算公式获得强目标的栅瓣和对称伪峰角度上对应的各频率点的幅度谱强度信息,通过在方位、频率维度加权的方式对强目标的栅瓣和伪峰干扰进行抑制,来改善在强干扰下的弱目标检测能力。方法也适用于多目标情况,对各个目标依次使用,减少多目标时栅瓣和对称伪峰对方位历程的影响,净化了时间方位历程图。对所提方法给出了理论推导过程并进行了仿真和实验验证,实验结果验证可行。结果表明,新方法可以有效地抑制强目标的栅瓣和对称伪峰干扰,提高弱目标的检测能力,此外,净化后的时间方位历程图更利于弱目标检测和目标数量的估计。     

低信噪比线性调频信号目标的方位估计

线性调频(LFM)信号目标的方位估计是水声探测研究的重要内容,在进行方位估计时,若存在强干扰信号源与强背景噪声,阵元接收信号的信噪比会显著降低,严重影响LFM信号目标方位估计结果的准确性.针对该问题,提出了一种简明分数阶滤波方法,并将其与常规波束形成方法(CBF)相结合来实现低信噪比条件下LFM信号目标的方位估计.简明分数阶傅里叶变换能在正交角度上将LFM信号的能量聚集在特定频点处并形成明显的能量峰,利用该特性,可对阵列各阵元接收的低信噪比LFM信号在简明分数阶域聚集的能量峰进行最佳滤波,以滤除干扰信息及背景噪声.对滤波输出进行逆简明分数阶傅里叶变换可得到增强信干比和信噪比的阵元域信号,进一步用于目标方位估计,就能获得更加准确的目标方位。数值仿真结果和海试实验数据处理结果验证表明,本文所提出的方法可有效抑制干扰和背景噪声,并对低信噪比LFM信号进行准确、稳健的方位估计。     

基于自适应模板的低信噪比运动目标的自动定位算法

从目标相对于邻域背景区的信噪比出发,分析了其低信噪比运动小目标特性对自动目标识别所带来的困难.基于部分Hausdorff距离在模板匹配相似性度量中可以有效抑制背景与噪声干扰的优点,提出了用自适应模板实现自动目标识别的方法.然后,综合目标区像素灰度自适应二值化处理和形心算法,实现了图像序列中目标自动定位的功能.实验结果证明：该基于自适应模板的低信噪比运动目标的自动定位算法具有快速,稳定和实用等优点.     

提高复杂燃烧流场羟基示踪测速精度的方法

为了提高流速测量精度,研究OH荧光图像背景抑制的方法.分析识别复杂燃烧流场中存在的背景干扰,构建染噪的数值仿真模型;基于干扰图像和OH标记线信号图像的特性,采用空间变换思想,提出了自适应差分法消除发动机燃烧室剧烈反应区域的燃烧OH荧光干扰;利用空域上信号与背景残余的差别,采用空间滤波法优化背景抑制结果,最后对仿真模型和实验图片对比,验证了该方法的有效性,处理前后峰值信噪比提高了11.83dB,信噪比提高了8.66dB,速度计算误差改善到了1.2%.该方法可有效的抑制背景噪声,提高测速精度,满足激光诊断系统对测量精度的要求.     

利用多级时延估计与传递函数拟合的水下自干扰抑制法

提出了一种利用传递函数拟合与多级时延估计相结合的自干扰抑制方法来抑制接收信号中的强自干扰信号。首先通过频域解卷积估计发射泄漏的传递函数,进而构建出基本的对消信号,再通过互相关法与互谱法相结合的多级时延估计,精细补偿对消信号,获得良好的自干扰抑制效果。对实际海试数据的处理结果表明,当采用无人水下航行器(UUV)搭载一体化系统,在舷侧阵工作扇面内发射多帧线性调频(LFM)信号对目标船进行有源探测时,该方法的抑制比可达36 dB,且使得可检测回波的信干比从-30 dB降低到-55 dB,对后续的自干扰信号存在同样的抑制效果。以上结果表明该方法可抑制强自干扰信号,呈现出了较强的稳定性,使得淹没于强自干扰信号中的回波信号得以检测。     

声屏蔽技术抗拖船干扰

提出了多途信道中的声屏蔽技术,并应用于抗拖船干扰,以克服传统的阵处理办法抑制拖船干扰时所存在的探测盲区问题。把拖曳线列阵接收的拖船干扰和同方位的目标信号在频域通过一个屏蔽聚焦权滤波器,在对干扰屏蔽的同时,对目标聚焦。针对聚焦权实用性差的缺点,又提出了屏蔽非聚焦权,并与常规波束形成做了比较。11元水平阵数据仿真结果表明,该技术提高了处理增益约5 dB;在抑制拖船干扰提高处理增益的同时,理论上也可以消除掉在该方向上强目标干扰形成的探测盲区。     

红外搜索系统中弱小目标检测算法研究

 复杂背景下低信噪比弱小目标的检测是红外搜索系统中的重点和难点,为解决红外搜索系统中杂波干扰多、目标信噪比低等问题,提出一种模板匹配滤波的目标检测方法。该算法在预测背景的同时,通过对图像背景灰度值进行动态的阈值处理,自适应地进行背景抑制。当背景包含较多复杂因素时,采用模板匹配滤波的目标检测方法,消除背景抑制后的残留杂波,实现弱小目标的提取。试验结果表明：当场景较复杂且图像信噪比较低时,使用该算法处理后可使图像信噪比达到4 dB以上,从而提高了弱小目标的检测概率。     

An adaptive smooth unsaturated bistable stochastic resonance system and its application in rolling bearing fault diagnosis

An adaptive smooth unsaturated bistable stochastic resonance (ASUBSR) system for bearing fault signal detection is established. Based on the problem of output saturation and poor low-frequency suppression performance of classical bistable stochastic resonance (CBSR) system, an SUBSR with unsaturated characteristics is proposed. An ASUBSR system is designed by extracting the envelope spectrum of the input signal and resampling it to satisfy the adiabatic approximation condition, combining high-pass filter to filter out low-frequency interference, and using genetic algorithm to select the optimal system parameters. Through simulations and experiments, we found that the system can effectively suppress the interference of low-frequency and high-frequency, indicates that the system performs like a band-pass filter, and the output signal-to-noise ratio is better than that of the CBSR system. The proposed ASUBSR system has great application in the field of fault detection of rolling bearings.     

无源声呐多目标检测中反波束成形递推算法及其应用

随着声呐检测能力的提高,多目标干扰下微弱信号的检测问题日益突出。当声呐方位历程显示上出现多个干扰轨迹时,弱目标的检测显得十分困难。自适应噪声抵消(Adaptive Noise Canceling,ANC)技术为抑制多个干扰提供了理论基础,但是求解稳态最佳滤波矩阵存在着技术实现上的困难。
本文提出用一种反波束成形(Inverse Beamforming,IBF)递推算法,在阵元域逐一抵消多个强干扰,从而增强并提取出微弱目标信号。文中给出了递推求解由逆矩阵所表达的最佳滤波矢量的理论推导和相应的公式。利用IBF算法处理海试数据得到了较好的结果,显著改善了强干扰下对微弱信号的检测,甚至在普通波束成形(CBF)中未能显示出来的信号都可以被检测出来。     

Iterative inverse beamforming algorithm and its application in multiple targets detection of passive sonar

With the increasing of detection ability of passive sonar,the weak signal detection problem in multiple interferences becomes more and more important.In the time/bearing record(TBR) display of sonar detection,when there exist traces of multiple interferences,the identification of weak signal is difficult or impossible.The adaptive noise cancellation technique provides the theoretical basis for suppressing strong interferences.But the solution for finding the steady-state optimum filter matrix is quite difficult due to the real time calculation of inverse matrix of input data correlation matrix.The iterative inverse beamforming(IBF) algorithm for solving the optimum filter vector,which is expressed by inverse matrix of the ocean environment data,is derived in this paper,by which,the optimum filter can be eventually expressed as a sum of series simple matrices of constructed from sensor data.Based on the algorithm proposed in this paper,some examples of at sea experiment are provided.The strong interferences are cancelled and the weak signal is emerged,even it didn't appear in the conventional beamforming(CBF) processing.     

应用于拖线阵声呐的一种自适应旁瓣干扰抵消器的性能分析

拖线阵声呐要求在全方位上对强干扰进行抑制,但当强干扰是宽带并与信号方向偏离较大时,自适应干扰抵消器的性能会受到限制。本文对一种具有抽头延迟线结构的部分自适应旁瓣抵消器(PASC)在用于抑制大角度宽带干扰时的性能进行了分析。数值结果表明,在合理选取参数和结构的情况下,这种PASC在用来抵消一定带宽和偏离角的强干扰时,具有较好的抵消性能。     

Adaptive beamforming for nonstationary arrays

Adaptive beamformers automatically adjust to the input data on the sensors in an attempt to maximize the bearing resolution of a signal and minimize the effects of noise or interfering signals. To the author's knowledge, all adaptive beamformers currently available in the literature assume that the sensors are stationary over the time required to collect the data. This assumption is invalid on arrays of sensors mounted on nonstationary platforms, and results in poor beamforming performance. In this paper we present an adaptive beamformer that has been designed to operate on nonstationary arrays. The beamformer directly incorporates any changes in array shape or heading that may occur during the time required to collect the data. The output of the adaptive beamformer is shown for both synthetic data and for real data collected on a towed array. Results show that signal detection, bearing accuracy, bearing resolution, and interference suppression all increase when the array shape and track are incorporated into the beamformer if the sensor platform is not stationary.     

Identification of multiple transient faults based on the adaptive spectral kurtosis method

This paper proposes a multi-fault detection method based on the adaptive spectral kurtosis (ASK) analysis of the vibration signal from single sensor. A theoretical model of multiple bearing faults is established in this paper. Compared with the kurtogram and protrugram techniques, the proposed method can more effectively extract signatures of multiple bearing faults even in the presence of strong background noise. The performance of the proposed method in fault detection of the rolling element bearings is validated using simulation data and experimental signals from a bearing with multiple faults and two faulty bearings.     

水下目标前向声散射探测的干涉声场畸变量分析与应用

前向声散射探测中的信直比SDR;能衡量目标前向散射波与直达波的相对大小,但未考虑两种波的干涉叠加。前向散射波与直达波实际上是干涉在一起的且很难分离,因而信直比在目标探测分析中难以直接应用。以信直比为基础并计入前向散射波与直达波的干涉效果,引入了新的参量——干涉声场畸变量ΔFTL——并给出了理论计算公式。与SDR;相比,ΔFTL可以直接从声场数据中得到,在一定程度上具有不依赖先验信息的优势。以该公式为基础并结合千岛湖缩比目标探测试验数据,(1)通过估算几何扩展损失系数发现试验中的声波为球面波,结果与依据实测水文参数的射线声场模型仿真结果相符;(2)定量地证实了ΔFTL与目标穿越位置的对应关系,同时也证明了ΔFTL理论计算公式的有效性;(3)建立了一种新的直达波抑制效果评估方案,并完成了对自适应直达波抑制方法的效能评估。目标透声和航行姿态扰动对试验结果的影响可以忽略。分析结果表明:ΔFTL能够有效代替SDR,为开展前向声散射探测性能分析或直达波抑制效果评估提供理论参考。     

Motion compensation for adaptive horizontal line array processing

Large aperture horizontal line arrays have small resolution cells and can be used to separate a target signal from an interference signal by array beamforming. High-resolution adaptive array processing can be used to place a null at the interference signal so that the array gain can be much higher than that of conventional beamforming. But these nice features are significantly degraded by the source motion, which reduces the time period under which the environment can be considered stationary from the array processing point of view. For adaptive array processing, a large number of data samples are generally required to minimize the variance of the cross-spectral density, or the covariance matrix, between the array elements. For a moving source and interference, the penalty of integrating over a large number of samples is the spread of signal and interference energy to more than one or two eigenvalues. The signal and interference are no longer clearly identified by the eigenvectors and, consequently, the ability to suppress the interference suffers. We show in this paper that the effect of source motion can be compensated for the (signal) beam covariance matrix, thus allowing integration over a large number of data samples without loss in the signal beam power. We employ an equivalent of a rotating coordinate frame to track the signal bearing change and use the waveguide invariant theory to compensate the signal range change by frequency shifting.