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相关向量机在车辆行驶状态估计中的应用

, PP. 88-93

Keywords: 汽车工程,运动状态,估计,RVM,Kalman滤波

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Abstract:

针对车辆运动的非线性特性,利用比支持向量机(SVM)测试时间短、多样本时具有计算量小的相关向量机(RVM)对车辆行驶状态进行估计。为了能够较为准确地估计车辆行驶状态,采集实车试验数据,利用Kalman滤波器对采集到的车速和横摆角速度数据进行滤波,将滤波后的数据作为RVM的输入。依据贝叶斯理论建立最大似然函数,考虑到横摆角速度和车速变化的差异性,依据不同迭代次数下最大似然估计值、伽马值以及值的差异性确定最佳的迭代次数,保证模型具有较短的测试时间和较高的击中概率。有效性验证结果表明该模型能够较为准确地逼近待估计样本的真值,其中波动性较大的横摆角速度所需要的迭代次数更多,伽马值和值的变化更为迅速,收敛速度较快。

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