共查询到19条相似文献,搜索用时 484 毫秒
1.
2.
3.
4.
5.
6.
7.
8.
舰船辐射噪声的调制信息中包含了大量有关舰船特征的信息,作为被声呐识别和判断的依据,由此判别目标的类型和速度。传统的DEMON(Detection of Envelope Modulation On Noise)谱分析时,大多采用功率谱分析,在提取调制谱时,效果不太理想,存在对噪声抑制能力差、提取后的谐波特征不明显等缺点。论文提出了一种基于EMD分解和1(1/2)维谱分析的提取方法,首先对目标辐射噪声信号进行EMD(Empirical Mode Decomposition)分解得到原始信号的若干个固有模态分量(Intrinsic Mode Function,IMF),其次对这些IMF分量进行解调,然后对其中某几个分量作1(1/2)谱分析得到舰船轴频的基频及谐波信息。仿真和对实测数据分析的结果表明,该方法可以有效提取噪声的特征信息,具有良好的应用前景。 相似文献
9.
10.
11.
12.
13.
14.
15.
16.
文章研究了基于改进小波能熵和概率神经网络的水下目标识别方法。首先对水下目标辐射噪声信号进行小波变换多分辨率分解和重构,然后引入滑动时间窗,提取各分解子带在滑动时间窗内的改进小波能熵值作为目标识别的特征矢量,最后将特征矢量输入到概率神经网络中实现水下目标识别。对信号进行小波多分辨率分解可反映信号在不同频域上的特征,而引入滑动时间窗并在此基础上定义改进的小波能熵可反映信号的时域特征,因此改进小波能熵方法能同时反映信号的时频特征,更适合于水下目标特征提取。仿真结果表明了该方法的有效性。 相似文献
17.
研究了一种基于信号四阶累量对角切片谱的时频分布图的水中目标辐射噪声的线谱估计方法。分析了四阶累量对角切片谱具有的性质,即抑制高斯噪声和对称分布噪声;与功率谱具有相似的谱结构。介绍了Lofar处理的特点和Lofar谱图的计算步骤。结合Lo-far谱图和四阶累量对角切片谱的各自特点,提出了四阶累量对角切片谱图的处理方法。再利用图形学中的Radon变换,将谱图中的直线转换成Radon域中的峰值。得到峰值坐标后,可以计算谱图中的线谱频率等参数。仿真实例表明,该方法效果明显。 相似文献
18.
This paper proposes a new method for ship recognition and classification using sound produced and radiated underwater. To do so, a three-step procedure is proposed. First, the preprocessing operations are utilized to reduce noise effects and provide signal for feature extraction. Second, a binary image, made from frequency spectrum of signal segmentation, is formed to extract effective features. Third, a neural classifier is designed to classify the signals. Two approaches, the proposed method and the fractal-based method are compared and tested on real data. The comparative results indicated better recognition ability and more robust performance of the proposed method than the fractal-based method. Therefore, the proposed method could improve the recognition accuracy of underwater acoustic targets. 相似文献
19.
《船舶与海洋工程学报》2015,(3)
This paper proposes a new method for ship recognition and classification using sound produced and radiated underwater. To do so, a three-step procedure is proposed. First, the preprocessing operations are utilized to reduce noise effects and provide signal for feature extraction. Second, a binary image, made from frequency spectrum of signal segmentation, is formed to extract effective features. Third, a neural classifier is designed to classify the signals. Two approaches, the proposed method and the fractal-based method are compared and tested on real data. The comparative results indicated better recognition ability and more robust performance of the proposed method than the fractal-based method. Therefore, the proposed method could improve the recognition accuracy of underwater acoustic targets. 相似文献