高速公路交通状态的联合估计方法

提出一种有效的高速公路交通密度预测估计方法。通过分析交通模型，建立了系统的离散化状态和误差模型表达式，并采用推广Kalman滤波方法进行估计．为进一步增强数值稳定性以及提高计算效率，根据系统矩阵分块的特点，采用分块的正交化U-D分解算法实现时间更新，同时采用序列U-D分解方法进行测量更新．仿真计算和实际应用表明，该方法可以给出工程实用结果。

Associated estimation method for highway traffic states

An efficient method for estimating highway traffic flow is presented. Through analyzing the discrete-time model of traffic flow, an augmented state equation is built up and extended Kalman filter is used. To get high numerical stability and computational efficiency, modified weighted Gram-Schmidt orthogonal U-D factorization method is used for the time update of the extended Kalman filter. Simulation results and actual application show that the new method can be efficiently used to estimation and prediction highway traffic flows.