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A Recursive Non-Uniform Sampling Estimator for Asynchronous Nonlinear Systems.

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  • المؤلفون: Yang YH;Yang YH; Liu JG; Liu JG; Song SM; Song SM
  • المصدر:
    Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Apr 30; Vol. 24 (9). Date of Electronic Publication: 2024 Apr 30.
  • نوع النشر :
    Journal Article
  • اللغة:
    English
  • معلومة اضافية
    • المصدر:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: PubMed not MEDLINE; MEDLINE
    • بيانات النشر:
      Original Publication: Basel, Switzerland : MDPI, c2000-
    • نبذة مختصرة :
      In this paper, we consider the problem of asynchronous estimation in the presence of packet losses for the randomly sampling nonlinear system. Packet losses occur at the control input and at the measurement side. Firstly, the synchronization of the asynchronous sampling system is realized by weighting the state of the adjacent state update points. Secondly, the projection theorem is used to estimate the system state at the sampling time. Due to modeling errors and unmodeled dynamics, obtaining an accurate dynamic model is challenging. Therefore, observation inference based on interpolation techniques is proposed to solve the asynchronous estimation problem. Furthermore, the algorithm is extended to multi-sensor systems to obtain a distributed fusion estimator. Finally, simulation experiments are conducted to validate the effectiveness of the algorithm.
    • References:
      Sensors (Basel). 2024 Jan 05;24(2):. (PMID: 38257429)
    • Grant Information:
      The funding has name but no grant number Defense Industrial Technology Development Program
    • Contributed Indexing:
      Keywords: covariance intersection fusion; interpolation; modelling; non-uniform sampling; state estimation
    • الموضوع:
      Date Created: 20240511 Latest Revision: 20240513
    • الموضوع:
      20240513
    • الرقم المعرف:
      PMC11086129
    • الرقم المعرف:
      10.3390/s24092882
    • الرقم المعرف:
      38732988