实验表明此方法对水下岩石、烟囱等非结构化特性的目标的分类识别具有好的效果。
Experiments prove that this approach is effective to identify the nonstructural targets such as underwater rock and chimney.
最后研究了水下目标的分类识别问题。
Finally the target classification and identification are studied.
这一结果对于进一步开展水下目标信号的识别、分类研究具有重要参考价值。
The research results will be helpful in the further study of underwater acoustic signals.
本文研究了利用小波变换提取水下目标瞬态回波的边缘特征并进行目标分类的可行性。
The feasibilities of using wavelet transform to extract the edges of the echoes and then classify the targets based on the edge features have been studied in this paper.
在提取了水下目标信号的双谱特征的基础上，利用BP神经网络对五类水下目标信号的双谱特征进行分类，得到了较高的识别率。
Classifying five target signals underwater base on the bispectrum features through BP neural networks, the targets are identified properly with high efficiently.
水下目标舰船辐射噪声中有很强的线谱成份，这些成份对分类识别是非常有价值的，因此利用线谱特征对舰船目标进行识别是水声领域研究的重要内容。
Ship radiated noises include a lot of line-spectra which are valuable for targets identification. Therefore it becomes an important approach in the studies of underwater acoustics.
水下目标舰船辐射噪声中有很强的线谱成份，这些成份对分类识别是非常有价值的，因此利用线谱特征对舰船目标进行识别是水声领域研究的重要内容。
Ship radiated noises include a lot of line-spectra which are valuable for targets identification. Therefore it becomes an important approach in the studies of underwater acoustics.
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