A particle-inspired Monte Carlo tree estimation method is proposed to avoid repeating similar simulation and handle the
depletion problem in particle filter. Under the inspiration of particles, the method divides the state-space recursively in a
top-down manner to form a tree structure that each node in the tree is corresponding to a sub-space. Particles are
allocated to the corresponding terminal node during the procedure. Certain size of minimal sub-space or piece is
specified to terminate the dividing. Each piece is corresponding to a leaf-node of the tree structure and the prediction
probability density in it is approximated by the proportion of its particles in total particles. Instead of importance
sampling for each particle, the method takes uniformly random measurements to compute the posterior probability
density in each piece. As a result, the method is applied to growth model and has better performance in high SNR
environments compared with the Sampling Importance Resampling method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.