水声多径单阵元主动时间反转检测性能研究

利用时间反转(Time Reversal,TR)良好的环境宽容性以及能量聚焦性,单阵元收发合置换能器通过主动时反信号对低信噪比下目标进行检测,以经典射线理论建立海洋中声多径信道传播模型,基于理论推导了单阵元主动TR信号目标检测的可行性.从理论上对单阵元收发合置换能器主动TR的处理增益进行详细分析,推导出单阵元主动TR的...     

基于振动声调制和时间反转法的微裂纹定位检测研究

针对常规超声检测对闭合微裂纹不敏感的现象,开展了基于振动声调制技术的非线性超声检测研究,并引入时间反转法对检测信号中的非线性信号进行聚焦处理,实现闭合微裂纹的检测及定位。利用ABAQUS有限元软件仿真振动声调制技术检测铝管微裂纹,用有限长冲激响应滤波器提取检测信号中的一阶旁瓣非线性信号,在时域反转后重新加载到无裂纹铝管模型进行时反信号聚焦仿真。结果表明,振动声调制检测信号中的一阶旁瓣非线性信号能够在裂纹位置附近聚焦,管道径向布置的时间反转信号加载方式信号聚焦效果优于轴向布置,并且根据聚焦像的大小和位置可以对裂纹进行定量和定位。     

Moving target detection using super‐resolution algorithms with an ultra wideband radar

We present subspace based detection algorithms for detecting moving targets applied to the data collected by an ultra wideband radar system. Combining the results of time reversal MUSIC algorithm and delay estimation MUSIC methods resulted in successful localization of targets. We have investigated methods to resolve the ambiguities in target detection and we performed experiments using two targets to test the effectiveness of the algorithms. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 237–244, 2010     

基于时反选择性聚焦的信混比增强方法

将时反选择性聚焦方法应用于浅海波导中的信混比增强,提出了一种基于时反选择性聚焦的信混比增强方法。在没有探测声源的条件下,通过对返回的回波信号进行时反算子分解获得时反阵与目标及海底散射体之间的传输函数信息,取一个与目标对应的特征向量作为时反阵的激励权向量,可以实现声场在目标处的选择性聚焦,并抑制相应距离上的海底混响,从而增强回波信混比。该处理方法解决了直接时反处理需要探测声源和混响零点设置方法在海底处设置混响零点的同时也会在目标位置设置零点的问题。利用典型浅海波导环境,进行了计算机仿真实验．验证了该方法的有效性。     

基于MUSIC算法的复合材料近场冲击源定位

为了提高算法在近场冲击源的定位精度, 提出了基于多重信号分类 (MUSIC)算法的复合材料近场冲击源定位方法。首先通过Fresnel近似将一维远场模型扩展到二维近场模型; 接着应用Gabor小波变换从冲击的宽带信号中提取某一中心频率的窄带波; 最后应用二维近场MUSIC算法, 扫描整个监测区域, 可以同时得到冲击源的距离和方位角。复合材料层合板的冲击实验结果表明该方法能够精确有效地对冲击源进行定位, 距离的定位误差不超过0.94 cm, 方位角误差不超过1°。     

二维MUSIC近场被动定位方法

介绍了一种高精度的近场被动定位方法——二维MUSIC被动定位方法。它是一种在距离和方位上进行二维联合搜索的高精度被动定位方法。将MUSIC（多重信号分类）算法与近场聚焦波束形成方法相结合,能大大提高对近场目标的定位精度。先推导了基于均匀线列阵的二维MUSIC近场被动定位方法的定位原理,通过仿真比较了二维MUSIC被动定位方法与常规聚焦波束形成的定位性能,仿真表明,二维MUSIC被动定位方法的定位性能要明显高于常规的聚焦波束形成被动定位方法。并仿真了该方法在不同的阵元间距以及不同的目标距离时的定位性能,验证了该方法的可行性和有效性。     

Radar target classification method with reduced aspect dependency and improved noise performance using multiple signal classification algorithm

This study introduces a novel aspect and polarisation invariant radar target classification method based on the use of multiple signal classification (MUSIC) algorithm for feature extraction. In the suggested method, for each candidate target at each designated reference aspect, feature matrices called `MUSIC spectrum matrices (MSMs)? are constructed using the target?s scattered data at different late-time intervals. An individual MSM corresponds to a map of a target?s natural resonance-related power distribution over the complex frequency plane under the chosen aspect angle/late-time interval conditions. The collection of these feature matrices is used first to determine the best late-time interval for optimal feature extraction. Then, the MSM of a target, which are computed over the optimal time interval at all reference aspects, are superposed to obtain the `fused MUSIC spectrum matrix (FMSM)'. The FMSM of a target is its main classifier feature in the proposed method as the aspect dependency of an FMSM is highly reduced because of its multi-aspect construction process. The suggested method is demonstrated for both simple and complex target geometries such as conducting spheres, dielectric spheres and small-scale aircraft targets with high accuracy rates even for low SNR values using feature fusion at only a few different reference aspects.     

基于虚拟时间反转的高分辨率复合材料板结构损伤成像

针对传统时间反转(TR)方法消除Lamb波传播时间和操作复杂的问题, 提出了虚拟时间反转(VTR)方法。该方法采用换元激励和接收机制以保留时间信息, 并运用信号运算代替物理TR操作。讨论了基于阶跃激励获取损伤监测路径传递函数的VTR实现过程。提出了基于VTR的高分辨率损伤成像方法。为了降低复合材料各向异性的影响, 在成像中进行了速度修正。实验结果表明, VTR方法能对碳纤维复合材料板中的Lamb波损伤散射波包进行部分再压缩和TR聚焦, 通过本文中成像方法也能清晰显示出板中单个和两个近邻损伤。      

改进的被动时间反转阵处理水下通信

时间反转技术由于不受环境制约,能自适应实现信道多途压缩和对指定点的空间聚焦,降低多用户通信过程中的码间干扰和同道干扰,广泛应用在水声通信领域。利用垂直阵加权处理的方法,在被动时间反转的基础上增加了一抵消项,抑制其中一个用户在接收阵的接收幅度以达到对另一用户指定通信的目的。模拟仿真显示该方法比单独只用被动时间反转处理,误码率能降低一半。海上试验数据处理结果与模拟仿真基本一致。     

基于多重信号分类算法的复合材料冲击定位

为了提高复合材料结构冲击定位的精度和实时性, 将阵列信号处理技术引入到结构健康监测领域, 提出了利用小波变换和多重信号分类算法实现复合材料结构冲击定位的新方法: 通过小波变换提取冲击响应信号某一窄带频率成分, 运用多重信号分类(MUSIC) 算法实现冲击源到达方向的估计; 根据Lamb 波传播特性, 用小波变换求出某一中心频率下的对称模式和反对称模式的Lamb 波到达同一传感器的时间差, 结合对称模式和反对称模式Lamb 的速度差就可以估计出冲击源到达传感器的距离, 实现冲击定位。对玻璃纤维/环氧树脂复合材料层合板和碳纤维/双马树脂基复合材料层合板2 种试件的实验均表明该方法能快速、精确地识别出冲击源位置。      

Sensing a buried resonant object by single-channel time reversal

Scaled laboratory experiments are conducted to assess the efficacy of iterative, single-channel time reversal for enhancement of monostatic returns from resonant spheres in the free field and buried in a sediment phantom. Experiments are performed in a water tank using a broad-band piston transducer operating between 0.4 and 1.5 MHz and calibrated using free surface reflections. Solid and hollow metallic spheres, 6.35 mm in diameter, are buried in a consolidation of 128-μm-mean- diameter spherical glass beads. The procedure consists of exciting the target object with a broadband pulse, sampling the return using a finite time window, reversing the signal in time, and using this reversed signal as the source waveform for the next interrogation. Results indicate that the spectrum of the returns rapidly converges to the dominant mode in the backscattering response of the target. Signal-to-noise enhancement of the target echo is demonstrated for a target at several burial depths. Images generated by scanning the transducer over the location of multiple buried targets demonstrate the ability of the technique to distinguish between targets of differing type and to yield an enhancement of different modes within the response of a single target as a function of transducer position and processing bandwidth.     

Efficient Strip-Mode SAR Raw-Data Simulator of Extended Scenes Included Moving Targets Based on Reversion of Series

The Synthetic Aperture Radar (SAR) raw data generator is required to the evaluation of focusing algorithms, moving target analysis, and hardware design. The time-domain SAR simulator can generate the accurate raw data but it needs much time. The frequency-domain simulator not only increases the efficiency but also considers the trajectory deviations of the radar. In addition, the raw signal of the extended scene included static and moving targets can be generated by some frequency-domain simulators. However, the existing simulators concentrate on the raw signal simulation of the static extended scene and moving targets at uniform speed mostly. As for the issue, the two-dimensional signal spectrum of moving targets with constant acceleration can be derived accurately based on the geometric model of a side-looking SAR and reversion of series. And a frequency-domain algorithm for SAR echo signal simulation is presented based on the two-dimensional signal spectrum. The raw data generated with proposed method is verified by several simulation experiments. In addition to reveal the efficiency of the presented frequency-domain SAR scene simulator, the computational complexity of the proposed method is compared with the time-domain approach using the complex multiplication. Numerical results demonstrate that the present method can reduce the computational time significantly without accuracy loss while simulating SAR raw data.     

基于MUSIC功率谱估计的直升机空中信号分析

针对直升机飞行的辐射噪声特征谱线进行分析,提出一种基于MUSIC算法的直升机声信号处理方法。首先阐述MUSIC谱估计方法,然后针对实测不同机型直升机的飞行辐射噪声数据,分别进行MUSIC算法和传统方法的频谱分析,对得到的结果进行分析比较,数据处理结果表明MUSIC算法能够抑制噪声,明显改善信噪比,并能够更为细致地体现直升机声信号的谐波特性。     

Adaptive chaotic maps for identification of complex targets

Identification of complex targets in radar or sonar is a difficult problem. The author shows how the parameters of a simple chaotic map may be optimised to increase the cross correlation of a transmitted signal with the reflection from one target while decreasing the cross correlation with another target. The targets consist of simple arrays of point scatterers. The author shows that the optimisation process produces a chaotic signal with a power spectrum that maximises the ratio of the cross correlation with one target to the cross correlation with a different target, however the author also shows that the specific time ordering of the chaotic signal is also important in determining the cross correlation. The performance of the chaotic signal is better than a random signal with the same power spectrum. The author also shows an example of applying these methods to targets that are more complex.     

Fast polarization reversal of proton spins in solid-state targets

The method of polarization reversal of proton spins in solid-state targets, by using the effect of spin superradiance is analysed theoretically. The main aim is to find out the most accurate way of calculating the time of reversal and the final reversal polarization. Three approaches are compared: one, based on the standard Bloch equations; another, using numerical simulations for finite-particle model; and an approach based on effective equations taking into account local spin fluctuations. The latter method is shown to be the most accurate. The minimal reversal time can be of the order of 10⁻⁵ – 10⁻⁴ s. The maximal final polarization can reach 80–90%, but can never be 100% complete. The proposed method only works from negative to positive polarization.     

基于鸡群算法的近场声源三维定位MUSIC算法

针对三维多重信号分类（Multiple Signal Classification，MUSIC）算法估计声源位置时计算速度慢，计算量大等缺点，提出了一种基于鸡群优化（Chicken Swarm Optimization，CSO）算法的近场声源三维定位算法。首先建立近场声源信号接收的数学模型，并选取三维MUSIC算法中的空间谱函数为文章算法中的适应度函数。通过不断迭代和局部搜索，以适应度值为指标对鸡群个体进行排序，最终得到最优鸡群个体的位置，即近场待测声源的坐标。仿真和实验结果表明：文中算法具有定位精度高、计算效率高、实时性好等优点，文中算法的平均用时仿真时为三维MUSIC算法平均用时的1.9%，实验时为三维MUSIC算法用时的3.2%。     

Multiple target detection using split spectrum processing and groupdelay moving entropy

The split spectrum processing technique obtains a frequency-diverse ensemble of narrow-band signals through a filterbank then recombines them nonlinearly to improve target visibility. Although split spectrum processing is an effective method for suppressing grain noise in ultrasonic nondestructive testing, its application was mainly limited to the detection of single targets or multiple targets having similar spectral characteristics. In this paper, the group delay moving entropy technique is introduced primarily to enhance the performance of split spectrum processing in detecting multiple targets which exhibit different spectral characteristics (i.e., variations in target signal center frequency and bandwidth). This is likely to occur in complex, dispersive, and nonhomogeneous media such as composites, layered, and clad materials, etc. The analysis shows that the group delay moving entropy method can be used effectively to select the optimal frequency region for split spectrum processing when detecting such targets. Based on an iterative procedure that combines group delay moving entropy and split spectrum processing, multiple targets can be identified one at a time, and subsequently eliminated by using time domain windows. The removal of the dominant target improves the detection of the remaining weaker targets. Simulation results are presented which demonstrate the feasibility of the multistep split spectrum processing technique for detecting multiple targets in such materials     

盲源分离用于DOA估计研究

方位估计是水下目标定位、跟踪的前提。提出了一种复数域奇异值分解盲源分离方法,估计出了阵列流形,并利用阵列流形表现出的相位关系完成了目标方位估计。通过仿真实验与MUSIC高分辨方位估计方法和相关文献的方法进行了对比,结果表明该方法能很好地实现目标方位的实时估计,且性能更佳。     

聚焦矩阵在水中宽带目标被动跟踪中的应用

针对水中机动宽带目标,对于设计好的均匀线列阵,采用空间重采样方法计算基阵的恒定束宽阵元权系数,进而利用该阵元权系数产生聚焦矩阵,通过聚焦矩阵将不同频带的子带信号映射到同一参考频率上,然后将所有频率成分的信号功率谱密度矩阵作平均,并结合MUSIC(Multiple Signal Classification)算法,估计出目标的方位信息,从而实现水中宽带目标的被动跟踪。采用该方法进行仿真试验分析,结果表明在小孔径基阵上可实现宽带单目标的稳定测向被动跟踪,且对多目标具有一定的角被动分辨效果。     

Modeling Multi-Targets Sentiment Classification via Graph Convolutional Networks and Auxiliary Relation

Existing solutions do not work well when multi-targets coexist in a sentence. The reason is that the existing solution is usually to separate multiple targets and process them separately. If the original sentence has N target, the original sentence will be repeated for N times, and only one target will be processed each time. To some extent, this approach degenerates the fine-grained sentiment classification task into the sentencelevel sentiment classification task, and the research method of processing the target separately ignores the internal relation and interaction between the targets. Based on the above considerations, we proposes to use Graph Convolutional Network (GCN) to model and process multi-targets appearing in sentences at the same time based on the positional relationship, and then to construct a graph of the sentiment relationship between targets based on the difference of the sentiment polarity between target words. In addition to the standard target-dependent sentiment classification task, an auxiliary node relation classification task is constructed. Experiments demonstrate that our model achieves new comparable performance on the benchmark datasets: SemEval-2014 Task 4, i.e., reviews for restaurants and laptops. Furthermore, the method of dividing the target words into isolated individuals has disadvantages, and the multi-task learning model is beneficial to enhance the feature extraction ability and expression ability of the model.