Brief PaperA Multi-model Algorithm for Parameter Estimation of Time-varying Nonlinear Systems☆
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DeepMTT: A deep learning maneuvering target-tracking algorithm based on bidirectional LSTM network
2020, Information FusionCitation Excerpt :Many modified IMM algorithms [12–14] were proposed for specific scenarios of maneuvering-target tracking, such as nonlinear, non-Gaussian and multi-target tracking scenarios. On the other hand, model-set design algorithms were proposed to offer better model approximations in maneuvering-target tracking procedures, such as fixed structure MM (FSMM) [15,16] and variable structure MM (VSMM) [9,17] algorithms. Recently, advanced MM algorithms such as the MIE-BLUE-IMM [18] and hybrid grid MM (HGMM) [19] algorithms have been proposed, where more information, i.e., input estimation [18] and adaptive fine sub-models [19], is offered in the tracking processing.
An optimization approach to adaptive Kalman filtering
2011, AutomaticaCitation Excerpt :Since MMAE operates under the assumption that one of the models in the model bank is the right one, it is an unsuitable method for systems with unknown dynamics. It has been shown in the series of papers Baram and Sandell (1978), Li (1994, 2000), Li and Bar-Shalom (1994, 1996), Li and Jilkov (2005), Li, Jilkov, and Ru (2005), Li and Zhang (2000), Li, Zhang, and Zhi (1999), Li, Zwi, and Zwang (1999), Petridis and Kehagias (1998) and that using too large a model bank actually decreases performance since, in that case, the true model gets too much competition from false models. Therefore safeguarding with a lot of plausible system models is not an option.
Bayesian-based on-line applicability evaluation of neural network models in modeling automotive paint spray operations
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2024, IEEE Transactions on Automatic Control
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This paper was recommended for Publication in revised form by Associate Editor Brett Ninness under the direction of Editor Torsten Söderström.