• DocumentCode
    1649211
  • Title

    Improving the Gaussian Sum Particle filtering by MMSE constraint

  • Author

    Shao, Chao

  • Author_Institution
    Xian Inst. of Post & Telecommun., Xian
  • fYear
    2008
  • Firstpage
    34
  • Lastpage
    37
  • Abstract
    In the paper, we proposed a new modification to the Gaussian sum particle filtering (GSPF) by incorporating a minimum mean square error constraint (MMSE-GSPF) in tracking parameter variation of dynamic state space model, the algorithm complexity is reduced yet. In addition, the MMSE-GSPF also shows better performance in tracking of time-varying parameters than the original Gaussian sum particle filtering.
  • Keywords
    Gaussian processes; least mean squares methods; particle filtering (numerical methods); Gaussian sum particle filtering; MMSE constraint; dynamic state space model; minimum mean square error constraint; time-varying parameter; Decision support systems; Filtering algorithms; Gaussian noise; Hidden Markov models; Mean square error methods; Particle tracking; Predictive models; Signal processing algorithms; State estimation; State-space methods; Dynamic State Space model; Gaussian Sum Particle filtering; Gaussian Sum filtering; MMSE-GSPF;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing, 2008. ICSP 2008. 9th International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-2178-7
  • Electronic_ISBN
    978-1-4244-2179-4
  • Type

    conf

  • DOI
    10.1109/ICOSP.2008.4697062
  • Filename
    4697062
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1649211