The invention discloses a vehicle key driving state estimation method based on low-orbit satellite and vehicle-mounted sensing multi-source information fusion, which comprises the following steps: acquiring the real-time longitudinal speed of a vehicle by combining a Doppler frequency shift formula based on a low-orbit satellite measurement signal; acquiring real-time lateral force of the tire based on the intelligent tire measurement signal in combination with a Gaussian process regression algorithm; a tire slip angle is calculated based on a vehicle-mounted sensing signal, a tire cornering stiffness estimation algorithm is established in combination with lateral force prior information, and a real-time cornering stiffness adaptive value is obtained; vehicle parameters and real-time sensing signals are obtained, and a vehicle dynamics model is established in combination with the self-adaptive value of the cornering stiffness; a lateral force estimation value provided by an intelligent tire system, a longitudinal vehicle speed value measured by a low-orbit satellite, a yaw velocity and a transverse acceleration sensing measurement value serve as measurement signals, an innovation self-adaptive expansion Kalman filter is combined, and a vehicle key driving state estimation algorithm is designed. And the real-time side slip angle, the yaw velocity and the longitudinal velocity of the vehicle are obtained.
本发明公开了基于低轨卫星与车载传感多源信息融合的车辆关键行驶状态估计方法,包括:基于低轨卫星测量信号,结合多普勒频移公式,获取车辆实时纵向速度;基于智能轮胎测量信号,结合高斯过程回归算法,获取轮胎实时侧向力;基于车载传感信号计算轮胎侧偏角,结合侧向力先验信息建立轮胎侧偏刚度估算算法,获取实时侧偏刚度自适应值;获取车辆参数和实时传感信号,结合侧偏刚度自适应值,建立车辆动力学模型;以智能轮胎系统提供的侧向力估计值、低轨卫星测量的纵向车速值以及横摆角速度和横向加速度传感测量值作为测量信号,结合新息自适应拓展卡尔曼滤波器,设计车辆关键行驶状态估计算法,获取车辆实时质心侧偏角、横摆角速度和纵向速度。
Vehicle key driving state estimation method based on low-orbit satellite and vehicle-mounted sensing multi-source information fusion
基于低轨卫星与车载传感多源信息融合的车辆关键行驶状态估计方法
13.05.2025
Patent
Elektronische Ressource
Chinesisch
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