共查询到16条相似文献,搜索用时 187 毫秒
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在计算海底混响信号时,根据混响产生的物理机理,以射线声学为基础,用 Lambert散射定律计算海底反向散射强度,采用单元散射模型建立海底混响信号模型。利用 GPU相比于 CPU具有更高的浮点运算能力和内存带宽的特点,采用 GPU进行计算海底混响信号。通过对仿真混响信号的处理分析,在散射点较少时,混响信号包络更接近于 K分布,而随着散射点的增多,混响信号的包络接近于瑞利分布。符合混响信号的一般统计特性。该方法能快速仿真出混响信号,达到高效的目的,为以后混响信号的实时演示验证提供一条可供选择的途径。 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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建立了一个实际混响室的三维电磁模型,采用有限元数值方法对混响室内的电磁场分布进行了求解,并运用统计方法对电磁场数据进行处理,获取了相关系数和标准偏差等场均匀性统计信息.搅拌器是影响混响室场均匀性参数的重要部件,本文对于不同几何外形、回转半径和数量的搅拌器的混响室电磁场分布进行了数字仿真,并对比分析了搅拌器参数对于相关系数和标准偏差等统计信息的影响效果.为了验证仿真结果的准确性,本文将数值仿真结果与实际混响室的测试数据进行了对比,包括电磁场分布和场均匀性统计参数两方面,发现二者比较一致,仿真计算准确度高. 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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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 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