Aiming at the existing problems that object tracking algorithm fails to track under the
influence of occlusion conditions, the paper has improved the Kernel Correlation Filter …

Bilinear system is a special class of nonlinear system. This paper is concerned with the
identification problem of the bilinear systems in the state–space form. By treating the …

Now in its second edition, this accessible text presents a unified Bayesian treatment of state-
of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state …

Vehicles commuting over bridge structures respond dynamically to the bridge's vibrations.
An acceleration signal collected within a moving vehicle contains a trace of the bridge's …

Novel Student's t based approaches for formulating a filter and smoother, which utilize heavy
tailed process and measurement noise models, are found through approximations of the …

This paper presents a tutorial overview of path integral (PI) approaches for stochastic
optimal control and trajectory optimization. We concisely summarize the theoretical …

This book examines Gaussian processes in both model-based reinforcement learning (RL)
and inference in nonlinear dynamic systems. First, we introduce PILCO, a fully Bayesian …

Nonlinear Kalman filter and Rauch-Tung-Striebel smoother type recursive estimators for
nonlinear discrete-time state space models with multivariate Student's t-distributed …

The cubature Kalman filter (CKF) is a relatively new addition to derivative-free approximate
Bayesian filters built under the Gaussian assumption. This paper extends the CKF theory to …

This work presents a robust and computationally efficient algorithm for both whole-building
and component-level energy fault detection and diagnosis (FDD). The algorithm is able to …