【摘要】 为准确估计车辆的行驶速度,保证汽车的安全性,设计了基于无味卡尔曼滤波算法(UKF:Unscented Kalman Filter)的车速估计器,并与基于卡尔曼滤波(KF:Kalman Filter)算法所建立的估计器进行了比较。两个估计器都以七自由度整车模型为研究平台,同时在Matlab中搭建了UKF和KF的算法模型。仿真实验结果表明,当系统输入产生突变时,UKF算法与真实值的绝对误差率始终在4%以内,比KF算法的误差率大约降低了3%,UKF车速估计器能很好地预测车速变化的趋势,相对于KF估计算法效果更佳。更多还原

【Abstract】 Obtaining vehicle velocity information accurately is of great importance to guarantee the safety when driving. In order to estimate the vehicle velocity,a velocity estimator was designed based on UKF( Unscented Kalman Filter) algorithm,and a comparision with the estimator based on KF( Kalman Filter) algorithm was made.Both the estimators took vehicle model with seven degrees of freedom as platform,and the models of UKF and KF algorithms were established in Matlab,then a comparative analysis experiment was done. The result shows that when the input produces mutations,the absolute error rate between UKF algorithm and real value is always less than 4 percent,the error rate dropped by 3 points compared to KF. The simulation result proves that UKF speed estimator can forecast vehicle velocity change tendency accurately,the performance is better than KF.更多还原