Home > Research > Publications & Outputs > Sequential Monte Carlo methods for state and pa...


Text available via DOI:

View graph of relations

Sequential Monte Carlo methods for state and parameter estimation in abruptly changing environments

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>1/03/2014
<mark>Journal</mark>IEEE Transactions on Signal Processing
Issue number5
Number of pages11
Pages (from-to)1245-1255
Publication StatusPublished
Early online date23/12/13
<mark>Original language</mark>English


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 developed approach is compared against the Interacting Multiple Model (IMM) filter for tracking a maneuvering target over a complex maneuvering scenario with nonlinear observations. In the IMM filter a large combination of models is required to account for unknown parameters. In contrast, the proposed approach circumvents the combinatorial complexity of applying multiple models in the IMM filter through Bayesian parameter estimation techniques. The developed approach is validated over complex maneuvering scenarios where both the system parameters and measurement noise parameters are unknown. Accurate estimation results are presented.