The unscented Kalman filter (UKF) is a promising method for system state and structural parameters estimation. However, its performance depends on the process noise and measurement noise covariance matrices, which are usually unknown in practice. Arbitrary selection of these covariance matrices may lead to unreliable or even diverging estimation results. To resolve this critical problem, we propose a Bayesian probabilistic algorithm for the estimation of the noise covariance matrices based on the response measurement. The proposed Noise-Parameters-Identified Unscented Kalman Filter (NPI-UKF) has the following salient features: (1) the divergence problem is resolved; (2) reliable estimation results including uncertainty quantification can be obtained; and (3) NPI-UKF is applicable to nonstationary situations. These salient features are illustrated through the numerical applications to a bridge structure and a laboratory experiment to a shear building model. The efficacy and robustness of NPI-UKF will be validated.

无迹卡尔曼滤波器（UKF）是一种很有前途的系统状态和结构参数估计方法。然而，它的性能取决于过程噪声和测量噪声协方差矩阵，这在实践中通常是未知的。任意选择这些协方差矩阵可能会导致估计结果不可靠甚至发散。为了解决这个关键问题，我们提出了一种基于响应测量来估计噪声协方差矩阵的贝叶斯概率算法。所提出的噪声参数识别无迹卡尔曼滤波器（NPI-UKF）具有以下显着特点：（1）解决了发散问题；(2) 可以获得包括不确定性量化在内的可靠估计结果；(3) NPI-UKF适用于非平稳情况。这些显着特征通过对桥梁结构的数值应用和对剪切建筑模型的实验室实验进行了说明。NPI-UKF 的有效性和稳健性将得到验证。