基于车辆悬架振动响应的地面分类研究

为提升车辆在自然界地面上行驶时利用悬架振动响应识别地面类型的分类准确率，提出将时域参数特征和小波包能量特征相结合的特征提取方法，利用概率神经网络（PNN）对地面进行分类，对比了时域参数特征、小波包能量特征及两者结合的分类效果.试验中，使用道路模拟试验台输出6种路面时域高程，将单轴加速度传感器安装在车辆悬架摆臂上，采集垂向加速度信号.结果表明，使用提出的特征提取方法可以取得良好的分类效果，平均分类准确率达到了91.3%，明显优于其它两种方法. 

Study on Terrain Classification Based on Vehicle Suspension Vibration

In order to improve the terrain classification performance using the vibration response induced in suspension of a traversing vehicle on natural terrains,a new feature vector extraction method was proposed by combining time domain features and wavelet packet energy features.The probabilistic neural network(PNN)was utilized to perform classification,comparing the classification effect of the combined feature vector with the other two traditional ones.A road simulator was employed to perform the excitations of the presented six roads.The vibration data was collected by a single axis accelerometer mounted on the suspension arm perpendicular to the ground.The results indicate that the proposed method can result in a satisfactory classification accuracy of 91.3%,which outperforms the other two traditional ones.