Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments

@article{Nemeth2014SequentialMC,
  title={Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments},
  author={Christopher Nemeth and Paul Fearnhead and Lyudmila S. Mihaylova},
  journal={IEEE Transactions on Signal Processing},
  year={2014},
  volume={62},
  pages={1245-1255}
}
This paper develops a novel sequential Monte Carlo (SMC) approach for joint state and parameter estimation that can deal efficiently with abruptly changing parameters which is a common case when tracking maneuvering targets. The approach combines Bayesian methods for dealing with change-points with methods for estimating static parameters within the SMC framework. The result is an approach that adaptively estimates the model parameters in accordance with changes to the target's trajectory. The… CONTINUE READING

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