The variable structure multiple-model（VSMM） estimation approach, one of the multiple-model（MM） estimation approaches, is popular in handling state estimation problems with mode uncertainties.In the VSMM algorithms...The variable structure multiple-model（VSMM） estimation approach, one of the multiple-model（MM） estimation approaches, is popular in handling state estimation problems with mode uncertainties.In the VSMM algorithms, the model sequence set adaptation（MSA） plays a key role.The MSA methods are challenged in both theory and practice for the target modes and the real observation error distributions are usually uncertain in practice.In this paper, a geometrical entropy（GE） measure is proposed so that the MSA is achieved on the minimum geometrical entropy（MGE） principle.Consequently, the minimum geometrical entropy multiple-model（MGEMM） framework is proposed, and two suboptimal algorithms, the particle filter k-means minimum geometrical entropy multiple-model algorithm（PF-KMGEMM） as well as the particle filter adaptive minimum geometrical entropy multiple-model algorithm（PF-AMGEMM）, are established for practical applications.The proposed algorithms are tested in three groups of maneuvering target tracking scenarios with mode and observation error distribution uncertainties.Numerical simulations have demonstrated that compared to several existing algorithms, the MGE-based algorithms can achieve more robust and accurate estimation results when the real observation error is inconsistent with a priori.展开更多
The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is ...The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.展开更多
基金supported by the National Natural Science Foundation of China (Nos.61427808, 61333009 and 61174024)
文摘The variable structure multiple-model（VSMM） estimation approach, one of the multiple-model（MM） estimation approaches, is popular in handling state estimation problems with mode uncertainties.In the VSMM algorithms, the model sequence set adaptation（MSA） plays a key role.The MSA methods are challenged in both theory and practice for the target modes and the real observation error distributions are usually uncertain in practice.In this paper, a geometrical entropy（GE） measure is proposed so that the MSA is achieved on the minimum geometrical entropy（MGE） principle.Consequently, the minimum geometrical entropy multiple-model（MGEMM） framework is proposed, and two suboptimal algorithms, the particle filter k-means minimum geometrical entropy multiple-model algorithm（PF-KMGEMM） as well as the particle filter adaptive minimum geometrical entropy multiple-model algorithm（PF-AMGEMM）, are established for practical applications.The proposed algorithms are tested in three groups of maneuvering target tracking scenarios with mode and observation error distribution uncertainties.Numerical simulations have demonstrated that compared to several existing algorithms, the MGE-based algorithms can achieve more robust and accurate estimation results when the real observation error is inconsistent with a priori.
基金This work was supported by the National Natural Sciences Foundation of China (No. 30470998)Natural Science Foundation of Jiangsu Province (No. BK2005087)NCET (No. NCET-05-0489)PCSIRT and the Talent Foundation of NAU.
基金supported in part by National Basic Research Program of China(No.2012CB821200)in part by the National Natural Science Foundation of China(No.61174024)
文摘The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.
