Adaptive Variational Particle Filtering in Non-stationary Environments
This work addresses the challenge of efficient state estimation in dynamic systems, but it appears incremental as it adapts existing optimization methods to particle filtering.
The paper tackles the problem of particle filtering in non-stationary environments by connecting it to online mirror descent, resulting in an algorithm that achieves optimal particle efficiency.
Online convex optimization is a sequential prediction framework with the goal to track and adapt to the environment through evaluating proper convex loss functions. We study efficient particle filtering methods from the perspective of such a framework. We formulate an efficient particle filtering methods for the non-stationary environment by making connections with the online mirror descent algorithm which is known to be a universal online convex optimization algorithm. As a result of this connection, our proposed particle filtering algorithm proves to achieve optimal particle efficiency.