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改进强跟踪滤波算法及其在汽车状态估计中的应用

DOI: 10.3724/SP.J.1004.2012.01520, PP. 1520-1527

Keywords: 汽车动力学,状态估计,卡尔曼滤波器,强跟踪滤波器

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Abstract:

?准确实时地获取汽车行驶过程中的状态变量,对汽车底盘控制有着重要的意义,而这些关键状态往往难以直接测量或者成本较高.结合纵向、侧向和横摆三自由度非线性汽车模型,将改进强跟踪滤波(Improvedstrongtrackfilter,ISTF)算法应用到汽车的状态估计中,并改进了算法的稳定性.与扩展卡尔曼滤波(ExtendedKalmanfilter,EKF)算法进行比较分析.通过Carsim和Matlab/Simulink联合仿真和实车双移线实验验证算法,结果表明,该算法在估计精度、跟踪速度、抑制噪声等方面均优于扩展卡尔曼滤波算法,满足汽车状态估计器的软件性能要求.

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