A noise-immune Kalman filter for short-term traffic flow forecasting
A noise-immune Kalman filter for short-term traffic flow forecasting
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用于短期交通流预测的抗噪声卡尔曼滤波器
DOI：
10.1016/j.physa.2019.122601
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发表时间：
2019-12-15
影响因子：
3.3
通讯作者：
Qin, Jing
中科院分区：
文献类型：
--
作者：
Cai, Lingru;Zhang, Zhanchang;Qin, Jing
本文通过引入最大熵推导卡尔曼滤波器来实现交通流预测任务。传统的卡尔曼滤波基于最小均方误差，在高斯噪声下具有良好的滤波性能。然而，实际的交通流数据是由非高斯噪声填充的。在这种情况下，传统的卡尔曼滤波可能失效。根据最大熵准则推导出的卡尔曼滤波器对非高斯噪声不敏感，同时保留了传统卡尔曼滤波器的最优状态均值和协方差传播特性。为了实现这一目标，嵌入了一个不动点算法来更新最大熵卡尔曼滤波的后验估计。在四个基准数据集上的大量实验证明了该模型在交通流预测方面的优异性能。(C) 2019 Elsevier B.V.版权所有
This paper formulates the traffic flow forecasting task by introducing a maximum correntropy deduced Kalman filter. The traditional Kalman filter is based on minimum mean square error, which performs well under Gaussian noises. However, the real traffic flow data are fulfilled with non-Gaussian noises. The traditional Kalman filter may rot under this situation. The Kalman filter deduced by maximum correntropy criteria is insensitive to non-Gaussian noises, meanwhile retains the optimal state mean and covariance propagation of the traditional Kalman filter. To achieve this, a fix-point algorithm is embedded to update the posterior estimations of maximum correntropy deduced Kalman filter. Extensive experiments on four benchmark datasets demonstrate the outperformance of this model for traffic flow forecasting. (C) 2019 Elsevier B.V. All rights reserved.