Multiple-Model Filtering For Nonlinear Estimation
Authors: 
Feng Yang, Li HanAuthors Info & Claims
Feng Yang, Li HanAuthors Info & ClaimsPages 600 - 605
Abstract
A new Multiple-Model filter has been developed to solve the problem of state estimation for complex systems with large initial state error or strong nonlinearities. The main idea of the algorithm is to construct multiple Gaussian distributions by decomposing the Gaussian distribution in order to increase the level of detail of probability density and obtain a closed form solution. In proposed algorithm, the first two moments of priori distribution is characterized by designing multiple Gaussian distribution models. For each Gaussian distribution model, a nonlinear Kalman filter algorithm is used to perform time update and measurement update. Finally, the estimation of the state posterior distribution is calculated by a probability weighting method. The proposed algorithm has been tested on two simulation scenarios including a complex single-dimensional tracking and a classic multi-dimensional target tracking problem, which exhibited a good performance on state estimation.
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Index Terms
- Multiple-Model Filtering For Nonlinear Estimation
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March 2023
824 pages
ISBN:9781450399029
DOI:10.1145/3594315
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Published: 02 August 2023
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ICCAI 2023
ICCAI 2023: 2023 9th International Conference on Computing and Artificial Intelligence
March 17 - 20, 2023
Tianjin, China
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