matlab建立汽车模型代码-SDCN_Mar18_T2_P2_Unscented-Kalman-Filters:C++中的无迹卡尔曼滤波器。

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matlab建立汽车模型代码无味卡尔曼滤波器 写上去 优达学城课程,2017 年 10 月 自动驾驶汽车工程师纳米学位课程 “无味卡尔曼滤波器”项目,2018 年 3 月 克劳斯·H·拉斯穆森 使用 CTRV 运动模型在 C++ 中实现无迹卡尔曼滤波器。 两个自行车模拟数据集,数据集 1 和数据集 2(Ascii 文本文件),与 Term 2 Simulator 一起使用。 与扩展卡尔曼滤波器 (EKF) 一样,无迹卡尔曼滤波器 (UKF) 具有相同的三个步骤: 初始化 预言 更新 这些步骤编码在 ukf.cpp 文件中。 本项目使用了以下初始化参数: 初始状态协方差矩阵P_ = 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1 过程噪声标准偏差纵向加速度,单位为 m/s^2 std_a_ = 5 过程噪声标准偏差偏航加速度 rad/s^2 std_yawdd_ = 0.4 通过将预测的 UKF 值与测试数据集提供的 Ground True 值进行比较,计算位置 X & Y 和速度 VX

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