This paper introduces a novel method for real-time simultaneous input-state-parameter estimation with modulated colored noise excitation. In this method, the unknown inputs are modeled as modulated colored noise, which is realistic for real environmental excitations. By considering this input model, the typical problem of low-frequency drifts in the inputs and displacement responses estimation can be eliminated. The proposed approach provides a joint input-state-parameter estimation algorithm in a real-time manner by introducing the corresponding modeling equation for the unknown inputs into the extended Kalman filter. A Bayesian methodology is utilized to recursively update the noise covariance matrices in the filtering so stationarity of the input/response is not required for the proposed algorithm. Examples using truss and bridge models under different stationary scenarios are investigated. Compared with other existing methods that suffer from severe low-frequency drifts using only noisy acceleration measurements, the presented approach achieves substantially stable and accurate estimation for inputs, structural states and parameters.

本文介绍了一种使用调制有色噪声激励进行实时同步输入状态参数估计的新方法。在这种方法中，未知输入被建模为调制的有色噪声，这对于真实的环境激励是现实的。通过考虑这种输入模型，可以消除输入和位移响应估计中低频漂移的典型问题。所提出的方法通过将未知输入的相应建模方程引入扩展卡尔曼滤波器，以实时方式提供联合输入-状态-参数估计算法。贝叶斯方法用于递归更新滤波中的噪声协方差矩阵，因此所提出的算法不需要输入/响应的平稳性。研究了在不同静止场景下使用桁架和桥梁模型的示例。与仅使用噪声加速度测量遭受严重低频漂移的其他现有方法相比，所提出的方法实现了对输入、结构状态和参数的基本稳定和准确的估计。