Azimi-Sadjadi M R, Yao D, Huang Q, Dobeck G J
Signal/Image Processing Laboratory, Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA.
IEEE Trans Neural Netw. 2000;11(3):784-94. doi: 10.1109/72.846748.
In this paper, a new subband-based classification scheme is developed for classifying underwater mines and mine-like targets from the acoustic backscattered signals. The system consists of a feature extractor using wavelet packets in conjunction with linear predictive coding (LPC), a feature selection scheme, and a backpropagation neural-network classifier. The data set used for this study consists of the backscattered signals from six different objects: two mine-like targets and four nontargets for several aspect angles. Simulation results on ten different noisy realizations and for signal-to-noise ratio (SNR) of 12 dB are presented. The receiver operating characteristic (ROC) curve of the classifier generated based on these results demonstrated excellent classification performance of the system. The generalization ability of the trained network was demonstrated by computing the error and classification rate statistics on a large data set. A multiaspect fusion scheme was also adopted in order to further improve the classification performance.
本文提出了一种基于子带的新分类方案，用于从声学反向散射信号中对水雷和类水雷目标进行分类。该系统由一个结合小波包和线性预测编码（LPC）的特征提取器、一个特征选择方案以及一个反向传播神经网络分类器组成。本研究使用的数据集包括来自六个不同物体的反向散射信号：两个类水雷目标和四个非目标物体在多个方位角下的信号。给出了在十种不同噪声实现情况下以及信噪比（SNR）为12 dB时的仿真结果。基于这些结果生成的分类器的接收器操作特性（ROC）曲线表明该系统具有出色的分类性能。通过计算大数据集上的误差和分类率统计数据，证明了训练网络的泛化能力。为了进一步提高分类性能，还采用了多方位融合方案。
