Minimum entropy and feedback structure-based algorithm for variable structure multi-model fusion
Anke Xue · Control theory & applications · 2013
When applying the traditional variable structure multi-model algorithms(VSMM) to the state estimation problems of high maneuver and large observation error,one may face the difficulty of estimation degradation caused by the mismatch between the prior model sets and the real modes.To deal with this difficulty,a minimum entropy VSMM algorithm(MEVSMM) is proposed based on the principle of minimum entropy.First,all model-based estimations are fed back online.Second,the optimal solution is found if the distributions of the related estimations satisfy the minimum entropy condition.A sub-optimal algorithm(PF-MEVSMM) is also designed by employing the particle filter(PF) and the challenge-match algorithm(CM).Comparing to some existing VSMM algorithms,the results demonstrate that the proposed algorithm can provide refined model sets with smaller sizes,as well as more robust and accurate estimation results.