中文摘要：

针对城市区域快速路网，以实现交通流运行状态实时估计为目标，建立宏观交通流状态空间模型，在实现交通流状态估计的同时，更新交通流模型参数，提高交通流模型的适应性和准确性．然后提出了基于奇异值分解（SVD）的优化抗差无损卡尔曼滤波（UKF）算法，用奇异值分解代替标准UKF的Cholesky分解，解决了协方差矩阵非正定时滤波计算不能持续的问题，同时，该算法根据观测协方差矩阵是否病态选择抗差因子，对增益矩阵和观测协方差矩阵进行自适应计算，进而抑制由于模型较高的非线性带来的误差．通过实验证明，文中所提算法避免了扩展卡尔曼滤波（EKF）算法的滤波发散问题，能准确跟踪交通流的变化趋势，提高交通流状态估计的稳定性和精度．

英文摘要：

In order to realize the real-time traffic flow state estimation of the regional freeway network in cities, a macroscopic traffic flow state space model is constructed. This model helps to estimate the traffic flow states and up- date the model parameters, and it can improve the adaptability and accuracy of the traffic flow model. Then, the SVD （ Singular Value Decomposition） -based optimized robust UKF （ Unscented Kalman Filter） algorithm is pro- posed. In the algorithm, the singular value decomposition is adopted to replace the Cholesky decomposition, thus solving the problem that the filtering can＇t continue when the covariance matrix is non-positive. Meanwhile, differ- ent strategies are chosen according to whether the observation covariance matrix is pathological, and both the gain matrix and the observation covariance matrix are adaptively calculated. Furthermore, the error caused by the high nonlinearity of the constructed model is inhibited. Experimental results show that the proposed algorithm can avoid the filtering divergence of the EKF （ Extended Kalman Filter） algorithm and can accurately track the trend of the traffic flow, thus improving the stability and precision of the traffic flow state estimation.