KLD Sampling for Particle Filters - using Kullback-Leibler Distance

Version 1.1.0.0 (8.99 KB) by Kevin Nickels
This implements Dieter Fox's KLD-sampling algorithm to ensure a proper sample set
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Updated 18 Dec 2013

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When using particle filters to approximate an unknown distribution, how many samples should be used? Too few may not adequately sample the distribution, while too many can unacceptably increase the run-time.

Dieter Fox's KLD-sampling algorithm lets use adaptively estimate how many samples are needed. This class facilitates (implements) this method.

Citation:
Fox, Dieter. "Adapting the sample size in particle filters through KLD-sampling." The international Journal of robotics research 22.12 (2003): 985-1003.

Cite As

Kevin Nickels (2024). KLD Sampling for Particle Filters - using Kullback-Leibler Distance (https://www.mathworks.com/matlabcentral/fileexchange/44735-kld-sampling-for-particle-filters-using-kullback-leibler-distance), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2012a
Compatible with any release
Platform Compatibility
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Version Published Release Notes
1.1.0.0

BSD File
1.0.0.0