Combined Model Parameter and State Fusion Estimation Based on Iterative Least Square Method

For multi-sensor systems describing by state space models with unknown model parameters, the state estimation and model parameter estimation are required to work simultaneously. This paper focus on using the least square method to estimate the unknown model parameters and Kalman filter method to estimate the system states. Based on matric fusion rule, the estimation accuracy is improved. A numerical example is applied to confirm the effectiveness of the proposed algorithm.