混响背景下回波信号起始位置提取*

对回波信号起始位置的提取是水下目标检测和识别的关键因素之一。在实际工作中,主动声纳接收到的信号既包含真正的目标回波信号,也包含大量的海洋噪声和混响,而回波信号往往淹没在背景噪声中,因此难以准确提取回波信号的起始位置。论文将分数阶傅里叶变换引入到回波信号检测中,利用滑动窗对回波信号进行截取,当截取后的信号与发射信号在FRFT域的峰值位置相匹配时即可提取出回波信号的起始位置。仿真结果显示论文方法有较好的效果。     

基于盲源分离的一维距离像特征提取算法磁

在日趋复杂的电磁环境下,雷达回波中存在着大量的噪声和干扰,一维高分辨距离像目标识别过程中,采用一般的特征提取算法不能提取有效的目标特征,影响着目标识别的效果。论文采用盲源分离的方法对复杂电磁环境下的雷达回波进行处理,提取目标回波,将强散射点回波信号视为不同的信号源进行盲源分离,实现了一维距离像上散射点位置特征的提取,并通过计算机仿真验证了该方法的有效性。     

一种基于非相参雷达目标回波的特征提取算法

本文在着重研究了非相参雷达视频回波的基础上,提出了一种基于线性预测系数及离散K—L正交变换的雷达目标回波的特征提取算法。采用实地录取到的大量雷达目标数据对这种算法进行检验,实验结果表明,这种方法具有较高的识别率。     

数字信号相关法在船闸人字闸门母材厚度测量中的应用

针对超声测厚的精度问题,分析了超声回波时间差的识别计算方法,并提出了一种基于最小二乘多项式拟合的方法来提取相关系数峰值。该方法可兼顾回波时差的计算精度和计算复杂度,得到较准确的时差计算值。该方法应用到湘江二级航道二期工程二线船闸人字闸门母材厚度测量中,测量结果准确可靠,为人字闸门等金属结构的质量控制提供准确的数据支撑。     

雷达训练系统目标回波信号模拟方法研究

对光学区扩展目标的散射特性进行分析,建立了基于多散射中心及线性调频体制宽带雷达的目标回波模型,同时给出了雷达回波数据的存储模型及处理方法。在此基础上对扩展目标作径向和非径向匀速直线运动两种情况下的回波信号进行模拟仿真为验证回波模型的正确性,对仿真的回波数据分别进行脉冲压缩和多普勒处理,得出的目标的一维距离像和速度维正确地反映出目标特征,表明模型可行有效,为后续的目标特征提取、目标识别奠定了基础。     

舰船目标雷达回波的仿真和模拟分析

传统舰船目标雷达回波系统能够实现目标雷达回波的发送与接收,但回波频率多次折射发生回波衍射导致接收信号紊乱,为此设计舰船目标雷达回波实时模拟系统。优化雷达信号实时模拟器系统,完成多模块点对点接收;描述雷达串行多波束发射信号,实现雷达回波精准定位;针对运动扩展目标进行雷达回波频谱调制,通过目标特性分析,实现目标雷达回波实时模拟系统设计。实验数据表明,设计的雷达回波实时模拟系统比传统回波模拟系统的回波接受率高25%,说明设计的回波实时模拟系统具备极高的有效性。     

基于模拟退火技术的舰载米波搜索雷达信号的方向估计

本文阐述了舰载米波雷达天线阵转动时多目标回波信号的模型,提出了采用模拟退火线性优化神经网络实现舰载米波搜索雷达天线阵转动时多目标回波信号的方向估计。仿真结果表明该是有效的,并且具有速度高、准确的优点。     

大数据异步传输环境下舰船激光网络回波信号增强处理

针对信号异步传输所导致增强效果不理想问题,提出大数据异步传输环境下舰船激光网络回波信号增强处理方法。采用信号累积增强技术将舰船激光网络范围内若干个回波信号积累在一起,形成一个高强度的回波信号,增强回波信号被采集的概率,确定激光网络正常状态下的分形盒维数与实时网络连接分形盒维数的差,若差值大于阈值即可将实时网络的回波信号定义为异常信号清除掉;针对被清除的异常数据,采用贝叶斯因子分析模型恢复缺失的回波信号,并对回波信号进行去噪处理。实验结果显示,该方法异常信号漏检率与误检率均低于2%,能够彻底抑制信号内的噪声分量,较好地进行信号恢复。     

一种基于分数阶傅里叶变换的主动声自导回波检测方法

研究一种基于分数阶傅里叶变换(FRFT)的主动声自导回波信号检测方法.在主动声自导中chirp类信号是常用的主动目标探测信号之一.由于分数阶傅里叶变换在处理chirp类信号时具有巨大优势,提出一种新的chirp类回波信号检测方法,通过对回波信号进行FRFT处理与相关处理可实现对目标的有效检测.仿真结果表明,与传统拷贝相...     

深海船舶导航系统的回波信号研究

应用雷达回波进行深海船舶导航非常必要。本文研究导航系统中的回波信号,并且描述目标船舶的起伏特性、运动情况,最后在含有海杂波和噪声的回波数学模型中进行船舶雷达导航系统的仿真,仿真结果表明,通过船舶综合回波仿真信号可以有效反映起伏情况等船舶的回波影响。     

Classification of underwater target echoes based on auditory perception characteristics

In underwater target detection, the bottom reverberation has some of the same properties as the target echo, which has a great impact on the performance. It is essential to study the difference between target echo and reverberation. In this paper, based on the unique advantage of human listening ability on objects distinction, the Gammatone filter is taken as the auditory model. In addition, time-frequency perception features and auditory spectral features are extracted for active sonar target echo and bottom reverberation separation. The features of the experimental data have good concentration characteristics in the same class and have a large amount of differences between different classes, which shows that this method can effectively distinguish between the target echo and reverberation.     

基于听觉感知特征的水下目标回波分类(英文)

In underwater target detection, the bottom reverberation has some of the same properties as the target echo, which has a great impact on the performance. It is essential to study the difference between target echo and reverberation. In this paper based on the unique advantage of human listening ability on objects distinction, the Gammatone filter is taken as the auditory model. In addition, time-frequency perception features and auditory spectra features are extracted for active sonar target echo and bottom reverberation separation. The features of the experimental data have good concentration characteristics in the same class and have a large amount of differences between different classes, which shows that this method can effectively distinguish between the target echo and reverberation.     

掩埋目标声散射特征提取（英文）

Elastic acoustic scattering is important for buried target detection and identification. For elastic spherical objects, studies have shown that a series of narrowband energetic arrivals follow the first specular one. However, in practice, the elastic echo is rather weak because of the acoustic absorption, propagation loss, and reverberation, which makes it difficult to extract elastic scattering features, especially for buried targets. To remove the interference and enhance the elastic scattering, the de-chirping method was adopted here to address the target scattering echo when a linear frequency modulation(LFM) signal is transmitted. The parameters of the incident signal were known. With the de-chirping operation, a target echo was transformed into a cluster of narrowband signals, and the elastic components could be extracted with a band-pass filter and then recovered by remodulation.The simulation results indicate the feasibility of the elastic scattering extraction and recovery. The experimental result demonstrates that the interference was removed and the elastic scattering was visibly enhanced after de-chirping, which facilitates the subsequent resonance feature extraction for target classification and recognition.     

Extraction of echo characteristics of underwater target based on cepstrum method

The analysis and characteristic extraction of target echo characteristics are important in underwater target detection and recognition. Rigid acoustic scattering components are generally used as major echo contributors with relatively stable characteristic information. Previous studies focus on echo characteristics from a single angle, thereby limiting the amount of extracted characteristic information. This paper aims to establish a full-angle rigid echo components model and overcome the difficulty of the extraction of time delay characteristics of narrow-band acoustic scattering echoes. On the basis of the analysis of the target echo highlight model, the echo characteristics of rigid acoustic scattering components are extracted in the cepstrum domain, and a wavelet process is proposed to enhance the effect of time delay estimation. Experimental data indicate that the extracted time delay characteristics accord with the rigid echo characteristics of underwater target, thereby validating the effectiveness of the cepstrum method.     

倒谱域水下目标回波特征提取（英文）

The analysis and characteristic extraction of target echo characteristics are important in underwater target detection and recognition. Rigid acoustic scattering components are generally used as major echo contributors with relatively stable characteristic information. Previous studies focus on echo characteristics from a single angle, thereby limiting the amount of extracted characteristic information. This paper aims to establish a full-angle rigid echo components model and overcome the difficulty of the extraction of time delay characteristics of narrow-band acoustic scattering echoes. On the basis of the analysis of the target echo highlight model, the echo characteristics of rigid acoustic scattering components are extracted in the cepstrum domain, and a wavelet process is proposed to enhance the effect of time delay estimation. Experimental data indicate that the extracted time delay characteristics accord with the rigid echo characteristics of underwater target, thereby validating the effectiveness of the cepstrum method.     

超声回波信号的小波消噪研究

利用小波变换的时频局部化性质,对超声回波弱信号特征进行提取,得到了较好的边缘信号,从而对信号进行全面细致的分析。理论分析和仿真结果表明,小波分析方法适合于超声回波信号的检测和特征提取,在改善了信噪比的同时,还保持了很高的时间（空间）分辨力,具有准确度高、抗噪声性能好等优点。并将该技术应用于物距测量中,提高了系统测量的准确度。     

一种级联型自适应滤波器的混响抑制技术

在浅海环境中混响是造成主动声呐性能下降的主要原因之一。混响是由发射信号引起的,其频域上覆盖区域与发射信号基本重合,时域上与发射信号及目标回波强相关,这给混响和目标的分离造成了很大的困难。本文借鉴PD雷达中的动目标检测方法,提出一种适用于声呐动目标检测的滤波器设计算法。该算法利用运动目标回波和混响在时频域上的不同特性,设计了级联自适应滤波器实现混响抑制和目标增强。在此基础上进行匹配滤波等处理可以获得理想的效果。该算法可大幅提高信混比,有效改善运动目标的检测能力。     

点频干扰下的SAR回波模拟和成像

在噪声和点频干扰的影响下,研究了SAR回波模拟方法,给出了干扰信号回波的表达式,并以3×3点阵目标为研究对象,分析了不同干信比下的点频干扰对成像效果的影响,仿真表明理论分析的正确性,得到了点频干扰可以影响不同距离目标的成像效果的结论。     

目标覆盖层在典型浅海波导中的消声作用

应用非刚性表面声散射的物理声学方法,发展了基于射线跟踪技术的浅海波导中覆盖消声层目标回声计算的数值方法。选取Benchmark潜艇模型和两种假想的消声覆盖层(反射系数在垂直入射时相同,随角度增加的速率不同),讨论了自由空间和浅海信道中弱正梯度和负梯度两种典型声速剖面条件下刚性及覆盖消声层目标的回声特性。结果指出:由于自由空间和浅海波导中目标回声形成机理不同,这两种覆盖层对自由空间中的消声作用影响较小,对浅海波导中的消声作用影响较大;自由空间中消声覆盖层的消声作用不能完全反映其在浅海波导中的实际效果;在实际浅海波导中为了获得较好的消声作用,不仅要在0度入射角还应在小角度(0-30度左右)范围内将覆盖层的反射系数控制在较小的数值区间。     

基于多特征信息和直方图相交的改进Meanshift算法

针对Mean-Shift算法仅利用颜色信息并以Bhattacharya系数为相似性度量导致算法准确率较低的问题,提出一种结合颜色和纹理信息并以直方图交集为相似性度量的跟踪算法。首先用局部二元模式算子提取纹理信息,再以对数比加权直方图取代传统直方图构建目标和候选目标模型,然后根据场景自适应地综合两类特征并通过Mean-Shift算法获得目标的初略位置,最后以直方图交集作为相似性度量并用Powell方法求其极值作为目标在当前帧中位置的估计。实验表明,该算法不仅能增强跟踪的准确性,而且对光照弱、目标与背景颜色近似等情况也有较好的鲁棒性。