Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments
- Resource Type
- Periodical
- Authors
- Nemeth, C.; Fearnhead, P.; Mihaylova, L.
- Source
- IEEE Transactions on Signal Processing IEEE Trans. Signal Process. Signal Processing, IEEE Transactions on. 62(5):1245-1255 Mar, 2014
- Subject
- Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Computing and Processing
Parameter estimation
Bayes methods
Monte Carlo methods
Target tracking
Approximation methods
Vectors
Adaptation models
Sequential Monte Carlo methods
joint state and parameter estimation
nonlinear systems
particle learning
tracking maneuvering targets
- Language
- ISSN
- 1053-587X
1941-0476
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.