CAJ | 학술논문

研究了基于小波包变换和Fisher线性分类器的水下目标识别方法。小波包是在小波变换的基础上发展起来的时频分析方法，能够对非平稳信号提供更丰富的时频信息。通过对水下目标辐射噪声信号进行小波包分解，提取小波包分解的终端节点的熵值作为特征矢量，应用Fisher线性分类器设计的分段线性分类器对水下目标进行分类识别。仿真结果表明，以小波包熵作为特征矢量的分类方法具有较高的识别正确率。
연구료기우소파포변환화Fisher선성분류기적수하목표식별방법。소파포시재소파변환적기출상발전기래적시빈분석방법，능구대비평은신호제공경봉부적시빈신식。통과대수하목표복사조성신호진행소파포분해，제취소파포분해적종단절점적적치작위특정시량，응용Fisher선성분류기설계적분단선성분류기대수하목표진행분류식별。방진결과표명，이소파포적작위특정시량적분류방법구유교고적식별정학솔。
A method for underwater target recognition based on wavelet packet transform and fisher linear classifier is studied. On the basis of wavelet transform, the wavelet packet transform is developed. It can offer plentiful time-frequency information for nonstationary signals. Firstly, the radiated noise of underwater target is decomposed by wavelet packet. Secondly, the entropy of terminal nodes through wavelet packet is served as feature vectors. Lastly, the piecewise linear classifier which is designed based on Fisher linear classifier is applied for underwater target recognition. Simulation results show that the classification method which uses wavelet packet entropy as feature vectors possesses higher recognition cor-rect ratio.