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

TL;DR

This paper introduces a novel sequential Monte Carlo method that adaptively estimates states and parameters in environments with abrupt changes, improving tracking of maneuvering targets over traditional methods.

Contribution

The paper presents a new SMC approach combining Bayesian changepoint detection with static parameter estimation, reducing complexity compared to IMM filters.

Findings

Accurately tracks maneuvering targets with unknown parameters.

Outperforms IMM filter in complex maneuver scenarios.

Effectively estimates system and noise parameters during abrupt changes.

Abstract

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 changepoints with methods for estimating static parameters within the SMC framework. The result is an approach which 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.