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Design of the adaptive interacting multiple model algorithm
Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301), 2002Adaptive interacting multiple model (AIMM) estimation is a method to improve the interacting multiple model (IMM) estimation. But some new problems appear in the AIMM, such as how to select the structure of the adaptive model set, how to inherit the different datum of the filters based on the old model set. In the paper some instructive conclusions and
Yan He 0001 +2 more
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The Interacting Multiple Model Filter on Boxplus-Manifolds
2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2020The interacting multiple model filter is the standard in state estimation where different dynamic models are required to model the behavior of a system. It performs a probabilistic mixing of estimates. Up to now, it is undefined how to perform this mixing properly on manifold spaces, e.g. quaternions.
Tom L. Koller, Udo Frese
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Multiplicative Interaction in Generalized Linear Models
Biometrics, 1995Bilinear oI biadditive multiplicative models for interaction in two-way tables provide the major means for Studyiing g ,eniotype by cnvlirOnllllcmlt initer'actioni pioblems. In applicatioIns the typical accompanying assumptions are those of a noirnally clistributecl error and an identity link.
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An improvement to the interacting multiple model (IMM) algorithm
IEEE Transactions on Signal Processing, 2001Computing the optimal conditional mean state estimate for a jump Markov linear system requires exponential complexity, and hence, practical filtering algorithms are necessarily suboptimal. In the target tracking literature, suboptimal multiple-model filtering algorithms, such as the interacting multiple model (IMM) method and generalized pseudo ...
Leigh A. Johnston, Vikram Krishnamurthy
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Dialog Modeling for Multiple Devices and Multiple Interaction Modalities
2007Today a large variety of mobile interaction devices such as PDAs and mobile phones enforce the development of a wide range of user interfaces for each platform. The complexity even grows, when multiple interaction devices are used to perform the same task and when different modalities have to be supported.We introduce a new dialog model for the ...
Robbie Schaefer +2 more
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Performance Prediction of the Interacting Multiple Model Algorithm
1992 American Control Conference, 1992The Interacting Multiple Model (IMM) algorithm has been shown to be one of the most cost-effective hybrid state estimation schemes. Its performance, however, could only be evaluated via expensive Monte-Carlo simulations. An effective approach to the performance evaluation without recourse to simulations is presented in this paper.
X.R. Li, Y. Bar-Shalom
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Interacting multiple model road curvature estimation
2012 15th International IEEE Conference on Intelligent Transportation Systems, 2012Accurate road curvature estimation is essential to many drive assistance systems and active safety systems, such as curve speed warning, lane departure warning, and lane keeping assistance. To improve the overall performance of road curvature estimation and path prediction, we proposed an interacting multiple model (structure) approach in curvature ...
Truman Shen, Faroog Ibrahim
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Stability analysis of the Interacting Multiple Model algorithm
2008 American Control Conference, 2008The interacting multiple model (IMM) algorithm is a well-known state estimation algorithm for hybrid systems. We derive a lower bound and an upper bound for the error covariance of the IMM algorithm for controllable and observable hybrid systems. We then derive sufficient conditions for the exponential stability of the IMM algorithm for a special class
Chze Eng Seah, Inseok Hwang 0002
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Interactive Intent Modeling from Multiple Feedback Domains
Proceedings of the 21st International Conference on Intelligent User Interfaces, 2016In exploratory search, the user starts with an uncertain information need and provides relevance feedback to the system's suggestions to direct the search. The search system learns the user intent based on this feedback and employs it to recommend novel results.
Kaski Samuel +4 more
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Texture modeling by multiple pairwise pixel interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996A Markov random field model with a Gibbs probability distribution (GPD) is proposed for describing particular classes of grayscale images which can be called spatially uniform stochastic textures. The model takes into account only multiple short- and long-range pairwise interactions between the gray levels in the pixels. An effective learning scheme is
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