http://iet.metastore.ingenta.com
1887

Adapting sample size in particle filters through KLD-resampling

Adapting sample size in particle filters through KLD-resampling

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

An adaptive resampling method is provided. It determines the number of particles to resample so that the Kullback-Leibler distance (KLD) between the distribution of particles before resampling and after resampling does not exceed a pre-specified error bound. The basis of the method is the same as Fox's KLD-sampling but implemented differently. The KLD-sampling assumes that samples are coming from the true posterior distribution and ignores any mismatch between the true and the proposal distribution. In contrast, the KLD measure is incorporated into the resampling in which the distribution of interest is just the posterior distribution. That is to say, for sample size adjustment, it is more theoretically rigorous and practically flexible to measure the fit of the distribution represented by weighted particles based on KLD during resampling than in sampling. Simulations of target tracking demonstrate the efficiency of the method.

References

    1. 1)
      • 1. Straka, O., Simandl, M.: ‘A survey of sample size adaptation techniques for particle filters’. 15th IFAC Symp. on System Identification, Saint-Malo, France, July 2009, Vol. 15, pp. 13581363.
    2. 2)
      • 2. Cornebise, J., Moulines, É., Olsson, J.: ‘Adaptive methods for sequential importance sampling with application to state space models’, Stat. Comput., 2008, 18, (4), pp. 461480 (doi: 10.1007/s11222-008-9089-4).
    3. 3)
      • 3. Fox, D.: ‘Adapting the sample size in particle filters through KLD-sampling’, Int. J. Robot. Res., 2003, 22, (12), pp. 9851003 (doi: 10.1177/0278364903022012001).
    4. 4)
      • 4. Kwok, C., Fox, D., Meila, M.: ‘Adaptive real-time particle filters for robot localization’. IEEE Int. Conf. on Robotics and Automation, Taipei, Taiwan, September 2003, Vol. 2, pp. 28362841.
    5. 5)
      • 5. Soto, A.: ‘Self adaptive particle filter’. Proc. Int. Joint Conferences on Artificial Intelligence, Edinburgh, Scotland, July 2005, pp. 13981406.
    6. 6)
      • 6. Li, T., Sattar, T.P., Sun, S.: ‘Deterministic resampling: unbiased sampling to avoid sample impoverishment in particle filters’, Signal Process., 2012, 92, (7), pp. 16371645 (doi: 10.1016/j.sigpro.2011.12.019).
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2013.0233
Loading

Related content

content/journals/10.1049/el.2013.0233
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address