Classification of audio radar signals using radial basis function neural networks
Classification of audio radar signals using radial basis function neural networks
复制标题
使用径向基函数神经网络对音频雷达信号进行分类
DOI：
--
复制
发表时间：
2003
影响因子：
5.6
通讯作者：
V. Varadan
中科院分区：
文献类型：
--
作者：
Trent McConaghy;H. Leung;É. Bossé;V. Varadan
利用径向基函数(RBF)神经网络对坦克地面监视雷达采集的真实音频雷达信号进行分类。目前，需要操作员操作雷达系统，以识别从坦克、车辆、飞机等反弹的信号。该项目的目标是研究使用神经网络来执行这一目标识别任务的可能性，目的是减少坦克所需的人员数量。考虑了神经网络文献中不同的信号分类方法。第一种方法使用线性自回归(AR)模型来提取音频数据的线性特征，然后对这些特征进行分类，即AR系数。本文研究了基于最小二乘和高阶统计量的AR系数估计问题。第二种方法使用非线性预测器对音频数据建模，然后根据预测误差对信号进行分类。本文使用的真实音频雷达数据集是由AN/PPS-15地面监视雷达收集的，由13个不同的目标类别组成，包括行进中的男子、行走的男子、飞机、爬行的男子和船只等，发现每种分类方法都有一些难以分类的类别。总体而言，AR特征提取方法是最有效的，对训练数据的正确分类率为88%，对未用于训练的数据的正确分类率为67%。
Radial basis function (RBF) neural networks are used to classify real-life audio radar signals that are collected by a ground surveillance radar mounted on a tank. Currently, a human operator is required to operate the radar system to discern among signals bouncing off tanks, vehicles, planes, and so on. The objective of this project is to investigate the possibility of using a neural network to perform this target recognition task, with the aim of reducing the number of personnel required in a tank. Different signal classification methods in the neural net literature are considered. The first method employs a linear autoregressive (AR) model to extract linear features of the audio data, and then perform classification on these features, i.e, the AR coefficients. AR coefficient estimations based on least squares and higher order statistics are considered in this study. The second approach uses nonlinear predictors to model the audio data and then classifies the signals according to the prediction errors. The real-life audio radar data set used here was collected by an AN/PPS-15 ground surveillance radar and consists of 13 different target classes, which include men marching, a man walking, airplanes, a man crawling, and boats, etc. It is found that each classification method has some classes which are difficult to classify. Overall, the AR feature extraction approach is most effective and has a correct classification rate of 88% for the training data and 67% for data not used for training.