Multirate Sensor Fusion in the Presence of Irregular Measurements and Time-Varying Time Delays Using Synchronized, Neural, Extended Kalman Filters
Multirate Sensor Fusion in the Presence of Irregular Measurements and Time-Varying Time Delays Using Synchronized, Neural, Extended Kalman Filters
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使用同步神经扩展卡尔曼滤波器在存在不规则测量和时变时间延迟的情况下进行多速率传感器融合
DOI：
10.1109/tim.2021.3135537
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发表时间：
2022
影响因子：
5.6
通讯作者：
Biao Huang
中科院分区：
文献类型：
--
作者：
Jingyi Wang;Y. Alipouri;Biao Huang
传感器融合对于提高过程质量变量的估计精度起着至关重要的作用。为了补偿基于扩展卡尔曼滤波(EKF)的多速率传感器融合中的建模误差，本文综合了对偶神经扩展卡尔曼滤波(DNEKF)和状态模型补偿神经扩展卡尔曼滤波(SNEKF)。具体地说，融合是在存在具有时变时间延迟的不规则采样的低速测量的情况下执行的。该算法通过状态向量的增广来同时估计状态参数和神经网络参数。状态模型补偿神经网络(SNN)的估计参数由DNEKF和SNEKF共享。通过两个算例验证了该算法在不同条件下有效地减小了估计误差。此外，在均方误差方面，它成功地将快速软测量的关键工业质量变量的估计精度提高了20%以上，显示了其优越性。
Sensor fusion plays a critical role in improving estimation accuracy of process quality variables. In this article, the dual, neural, extended Kalman filter (DNEKF) and the state model compensation neural, extended Kalman filter (SNEKF) are synthesized to compensate for modeling errors in the extended Kalman filter (EKF)-based multirate sensor fusion. Specifically, fusion is performed in the presence of irregularly sampled, slow-rate measurements with time-varying time delays. The proposed algorithm estimates the state and neural network parameters simultaneously through state vector augmentation. The estimated parameters of the state model compensation neural network (SNN) are shared between the DNEKF and SNEKF. It is demonstrated through two numerical examples that the proposed algorithm effectively reduces the estimation error under different conditions. In addition, it successfully improves the critical industrial quality variable estimation accuracy from the fast-rate soft sensor for over 20%, in terms of the mean squared error, demonstrating its advantages.