Transfer State Estimator for Markovian Jump Linear Systems With Multirate Measurements
针对工业过程中快慢不同速率的测量数据，提出一种基于迁移学习的估计框架，利用慢速测量推导观测预测器，通过KL散度评估分布相关性，并设计迭代迁移状态估计器，提升估计精度。
In most industrial processes, some measurements are sampled frequently while other measurements are available infrequently and often slow rate. To utilize the slow rate measurements better for improving the accuracy of estimation, this article proposes a powerful unifying estimation framework for Markovian jump linear systems with multirate measurements based on the transfer learning strategy. Specifically, the form of knowledge transferred is designated as the observation predictor derived using the slow rate measurements. We define the universal evaluation of relatedness between the distribution transferred knowledge and ideal posterior distribution from the perspective of Kullback–Leibler (KL) divergence. A smoothing method is then proposed to compute one-step-behind posterior estimates of the state since the estimates obtained using the slow rate measurements are less than the fast ones. Based on this, an iterative transfer state estimator that includes the transferred observation predictor derived using the slow rate measurements is developed, whenever the slow rate measurements are available. Finally, a moving-target example and an experiment with GPS tracking for the ship-board echo sounder show that the proposed approach can be regarded as a competitive alternative of various existing fusion methods when slow rate measurements arrive.