The inverse heat conduction problems (IHCP) analysis method provides a promising approach for acquiring the thermal physical properties of materials, the boundary conditions and the initial conditions from the known temperature measurement data, where the efficiency of the inversion algorithms plays a crucial role in real applications. In this paper, an inversion model that simultaneously utilizes the process evolution information of the objects to be estimated and the measurement information is proposed. The original IHCP is formulated into a state-space problem, and the unscented Kalman filter (UKF) method is developed for solving the proposed inversion model. The implementation of the proposed method does not require the gradient vector, the Jacobian matrix or the Hessian matrix, and thus the computational complexity is decreased. Numerical simulations are implemented to evaluate the feasibility of the proposed algorithm. For the cases simulated in this paper, satisfactory results are obtained, which indicates that the proposed algorithm is successful in solving the IHCP.
