线性预测编码和支持向量机在目标识别中的应用

发布时间：2018-03-30 18:38

  本文选题：线性预测编码　切入点：支持向量机　出处：《舰船科学技术》2016年02期

【摘要】：针对利用船舶辐射噪声进行水下目标识别的问题进行研究,提出一种基于线性预测编码(LPC)倒谱系数和支持向量机(SVM)的船舶目标识别方法。该方法通过对捕获到船舶辐射噪声进行LPC倒谱分析,实现各信号分量及信道的分离,以提取其LPC倒谱参数。再采用支持向量机技术处理多类水下目标的非线性、小样本的识别分类。最后,利用仿真得到的几种水下目标辐射噪声进行本文算法试验,证明本文算法是有效的,并取得较高的识别准确率。
[Abstract]:Aiming at the problem of underwater target recognition using ship radiated noise, a ship target recognition method based on linear predictive coding (LPC) cepstrum number and support vector machine (SVM) is proposed.By analyzing the LPC cepstrum of the captured ship noise, the signal components and the channel are separated to extract the LPC cepstrum parameters.Then support vector machine (SVM) is used to deal with the nonlinear and small sample recognition and classification of multiple underwater targets.Finally, several underwater target radiated noises are simulated and tested in this paper. The results show that the proposed algorithm is effective and has a high recognition accuracy.
【作者单位】： 绍兴职业技术学院信息工程学院;
【分类号】：U675.7;U661.44
，

本文编号：1687134