Abstract:

 Dynamic Filtering models were based on state constrained with uncertainty by regarding uncertainty as state constraint, incorporated into the adjustment models. The unconstrained filtering algorithm is proposed according to unconstrained adjustment model, which is equal to standard kalman filtering algorithm. The inequality state constrained filtering algorithm and ellipsoid state constrained filtering algorithm are provided as the solution of state constrained adjustment models. With simulation calculation, it is compared that the results of different dynamic filtering algorithms based on state constrained with uncertainty are compared. The results show that dynamic filtering algorithms based on state constrained with uncertainty are better than kalman filtering algorithm, obtained simplicity and effectivity. 

Key words:

Uncertainty ,  Dynamic Filtering Algorithm ,  State Constraints ,  Kalman Filtering ,  Adjustment Model