共查询到19条相似文献,搜索用时 531 毫秒
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对于主动声纳探测浅海区域的慢速小目标而言,混响无疑是最为主要的干扰。由于混响信号主要源自于发射信号,频域上和发射信号本身频谱有着密切的关系,常规的匹配滤波器已不是最佳检测器。论文利用混响和目标回波之间的多普勒差异作为检测的特征量,介绍了主动声纳中的一种新的探测信号,M序列信号。即通过M序列对CW信号进行编码而扩宽其带宽,在保留CW信号敏感的多普勒特性的同时提高时延分辨力。文中借助模糊度函数以及Q函数作为分析工具,具体对比了传统的CW信号、LFM信号以及该信号对混响的抑制能力。Matlab仿真结果表明,该编码信号具有较之CW和LFM信号更好的抗混响能力以及更优的速度—时延分辨力,适用于浅海环境中对慢速小目标的探测。 相似文献
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针对浅海混响背景下频域自适应匹配滤波器检测性能下降的问题,提出基于分数阶傅里叶变换(FRFT)的频域自适应匹配滤波器检测方法,该方法利用模板匹配技术,采用滑动窗对接收信号进行最优阶次分数阶傅里叶变换,然后将此过程中得到的FRFT域图与参考信号最优阶次傅里叶变换FRFT域图进行匹配,将离差平方和作为评价相似度的指标,即对离差平方和最小值的位置进行滤波,并对滤波后的信号进行最优阶次分数阶傅里叶逆变换,从而实现混响背景下的目标检测。仿真结果表明,在信混比为-15 dB的情况下,该算法可显著提高频域自适应匹配滤波器的检测性能。 相似文献
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主动声呐发射波形设计研究 总被引:2,自引:1,他引:1
主动声呐可通过发射波形设计技术对抗信道衰落,以改善目标检测能力。因此发射波形设计是主动声呐系统领域的重要内容之一。本文综述近年来波形设计领域的研究成果,通过对各种发射信号的时域、频域及模糊度特性进行对比分析,得出不同信号体制的特点,为相关行业的工程应用提供参考。 相似文献
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混响是影响双基地或多基地声呐对水中目标探测识别的最主要干扰,提高抗混响能力对识别水中目标有极其重要意义.本文针对混响对目标回波的强时频干扰特性,基于分数阶傅里叶变换所具有的时频耦合分离特性,研究一种基于分数阶傅里叶变换的强混响抑制方法,并进行模拟仿真和水池测试研究,研究结果验证算法的有效性.本算法适用于信号形式为线性调频的宽带信号抗混响干扰,将脉冲信号在分数阶变换域进行尺度压缩,进而将目标信号和干扰信号在变换域中进行分离,有效达到抗混响的目的. 相似文献
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几种新型主动声呐发射信号性能分析研究 总被引:1,自引:1,他引:0
《舰船科学技术》2015,(11):103-107
主动声呐信号波形在主动声呐系统设计中占有十分重要的地位,声呐信号波形不仅决定了信号接收的处理方法,还直接决定了系统的时频分辨力、抗混响能力、目标跟踪等各方面性能。本文利用信号的模糊度函数和Q函数,研究了时下热点波形,包括梳状谱信号(SFM/PTFM)和雷达中研究相当成熟而在声呐中鲜有研究的复合声呐信号(LFM-Barker码),重点分析了其时频分辨力和抗混响性能。仿真及湖试结果表明对不同速度类型目标宜采用不同的发射信号波形,为主动声呐发射信号优选提供了参考和依据。 相似文献
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在浅海环境中,由于受到混响的影响,主动声呐接收到的信号混淆不清。针对上述问题,提出一种基于稀疏表示的非负矩阵分解(non-negative matrix factorization, NMF)抗混响方法。利用稀疏表示方法处理主动声呐回波信号,然后根据信号的稀疏性,构建基于非负矩阵分解的Kullback-Leibler(KL)问题,通过梯度下降法给出迭代规则,进而得到了目标信号矩阵中的协方差估计。仿真结果表明,相对其他去混响方法,该方法能够有效抑制混响,提高对水下目标的识别率。 相似文献
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水下目标检测具有重要意义,在军事和民用领域都发挥着重要作用。实际场景中可以获得的声呐图像非常有限,且声呐图像的信噪比较低,无法得到较好的检测结果。因此,本文引入小样本学习,基于Faster RCNN两阶段目标检测算法,选择不同的策略对模型进行优化,得到了较好的检测结果并验证了小样本目标检测在声呐图像领域的可行性。根据混响对声呐图像的影响进行仿真实验,得到不同混响背景下的声呐图像,对比分析了不同数据集下训练模型的检测性能。实验结果表明,在训练样本中增加混响信号可以提高低信噪比条件下的目标检测精度。 相似文献
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《船舶与海洋工程学报》2019,(3)
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. 相似文献
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基于单元散射理论介绍了瑞利分布模型和K分布模型,通过计算混响偏度和峰度判断出海底混响偏离瑞利分布模型,并利用CW信号、LFM信号的试验混响数据进行阵元域、波束域上的PDF曲线拟合。结果表明,海底混响的统计特性更趋向于K分布模型。利用BP网络方法和海底混响、点目标仿真信号的PDF特性进行了目标识别验证,其正确识别率达到了92%以上,且计算量大大降低。 相似文献
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The critical technical problem of underwater bottom object detection is founding a stable feature space for echo signals classification. The past literatures more focus on the characteristics of object echoes in feature space and reverberation is only treated as interference. In this paper, reverberation is considered as a kind of signal with steady characteristic, and the clustering of reverberation in frequency discrete wavelet transform (FDWT) feature space is studied. In order to extract the identifying information of echo signals, feature compression and cluster analysis are adopted in this paper, and the criterion of separability between object echoes and reverberation is given. The experimental data processing results show that reverberation has steady pattern in FDWT feature space which differs from that of object echoes. It is proven that there is separability between reverberation and object echoes. 相似文献
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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. 相似文献
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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. 相似文献
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