一种车辆机动目标跟踪的多传感器信息融合估计算法

A Multisensor Data Fusion Estimation Algorithm for Vehicle Maneuvering Target Tracking

为了保证自动高速公路系统对车辆机动目标的实时、精确跟踪,提出了一种车辆机动目标状态的多传感器信息融合估计算法.建立了车辆运动状态的离散时间多模式非线性动态系统模型,利用当前时刻的各个传感器量测数据,结合交互式多模型和扩展卡尔曼滤波器得到各个局部状态估计值和滤波误差协方差阵,并采用动态加权信息融合准则获得更为精确的车辆融合航迹估计值.通过仿真验证表明这种多传感器信息融合估计算法能实时有效地提高车辆机动目标跟踪精度.

To solve the vehicle＇ s tracking problems with real time and precision in automated highway system, a novel state estimation algorithm based on multisensor data fusion is proposed. Firstly, a nonlinear system dynamic model was established for the vehicle motion state with discrete time and multiple models. By utilizing each sensor measurement data in current time and combining interacting （ EKF）, the local state estimation value and error covariance multiple models （IMM） with extended kalman filter matrix were obtained. Then; data fusion with dynamic weight coefficient was carried out so that the accuracy of the state estimate in the maneuver could be improved to guarantee real-time tracking performance. The Monte-Carlo simulation results show the effectiveness of the algorithm in improving the accuracy of vehicle maneuvering target tracking.