Steady-state Kalman Fusion Filter Based on Improved Multi-innovation Least Squares Algorithm

Based on the improved multi-innovation least squares algorithm and Kalman filtering method, the state fusion estimation of multi-sensor systems with unknown parameters is studied. Firstly, an improved multi-innovation least squares algorithm is proposed to identify the unknown model parameters of the system. Then, based on the results of model parameter identification, the steady-state fusion Kalman filter of multi-sensor system is given by using the matrix weighted fusion criterion. A numerical simulation example verifies the effectiveness of the proposed algorithm.