The capability of a novel Kullback-Leibler divergence method is examined herein within the Kalman filter framework to select the input-parameter-state estimation execution with the most plausible results. This identification suffers from the uncertainty related to obtaining different results from different initial parameter set guesses, and the examined approach uses the information gained from the data in going from the prior to the posterior distribution to address the issue. Firstly, the Kalman filter is performed for a number of different initial parameter sets providing the system input-parameter-state estimation. Secondly, the resulting posterior distributions are compared simultaneously to the initial prior distributions using the Kullback-Leibler divergence. Finally, the identification with the least Kullback-Leibler divergence is selected as the one with the most plausible results. Importantly, the method is shown to select the better performed identification in linear, nonlinear, and limited information applications, providing a powerful tool for system monitoring.

翻译：本文在卡尔曼滤波器框架下研究了一种新颖的Kullback-Leibler散度方法，用于选择结果最合理的输入-参数-状态估计执行方案。该识别过程面临因不同初始参数集猜测导致结果差异的不确定性，所研究的方法利用从先验分布到后验分布过程中从数据获得的信息来解决此问题。首先，针对多个不同的初始参数集执行卡尔曼滤波，提供系统输入-参数-状态估计。其次，使用Kullback-Leibler散度将所得后验分布与初始先验分布进行同步比较。最后，选择Kullback-Leibler散度最小的识别结果作为最合理的结果。重要的是，该方法在线性、非线性和有限信息应用中均能选择性能更优的识别方案，为系统监测提供了有力工具。