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Hybrid state estimation and model-set design of invariable-structure semi-ballistic reentry vehicle

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Abstract

Multiple-model approach is one of the main streams for hybrid estimation. The difficulty of this approach to estimate the hybrid state of the semi-ballistic reentry vehicle (SBRV) is model-set design. This paper proposes a quasi-Monte Carlo model set that can ensure the estimator near-optimal in the sense of minimum mean square error (MMSE). The SBRV has a high nonlinearity and its mode is spanned by multiple parameters with known bounds. The design methods and characteristics of the quasi-Monte Carlo model set are given. The proposed model set has a higher accuracy than the model-set generated by the Monte Carlo method. The theoretical analysis and simulation results show the effectiveness and reasonability of the newly designed model set.

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Authors and Affiliations

  1. MOE KLINNS Lab, SKLMSE Lab, Institute of Integrated Automation, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    YongQi Liang & ChongZhao Han

  2. School of Aerospace, Xi’an Jiaotong University, Xi’an, 710049, China

    YongQi Liang

Authors
  1. YongQi Liang
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  2. ChongZhao Han
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Correspondence to YongQi Liang.

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Liang, Y., Han, C. Hybrid state estimation and model-set design of invariable-structure semi-ballistic reentry vehicle. Sci. China Inf. Sci. 54, 812–823 (2011). https://doi.org/10.1007/s11432-010-4159-6

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  • DOI: https://doi.org/10.1007/s11432-010-4159-6

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