Noise Adaptive Particle Filtering: A Hierarchical Perspective
2013 (English)Conference paper, Abstract (Other academic)
Optimal estimation problems for general state space models do not typically admit a closed form solution. However, modern Monte Carlo methods have paved the way to solve such complex inference problems. Particle filters (PF) are a popular class of such Monte Carlo based Bayesian algorithms, which solve the estimation problems numerically in a sequential manner.
PF in general, assume a prior knowledge of the (process and observation) noise distributions involving the state space model, whereas the properties of the noise processes are often unknown for many practical problems. Furthermore, the unknown noise distributions may be state dependent or even non-stationary, which prevent the offline noise calibrations.
In this article, the unknown noises are assumed to be slowly varying in time. The article then proposes a hierarchical noise adaptive PF where a two tier PF is run, the top tier PF estimates the latent states from the streaming observations and the bottom tier PF estimates the noise statistics conditioned on the top tier PF output together with the observations. The estimates are statistically fused together for the inference purpose. In essence, it is an implementation of approximate Rao-Blackwellized PF, where the later is achieved through local Monte Carlo integration. This approach is very generic for different noise classes and importantly, it enhances the level of parallelism in PF implementations.
Place, publisher, year, edition, pages
hierarchical particle filtering, noise adaptive filtering
IdentifiersURN: urn:nbn:se:liu:diva-90723OAI: oai:DiVA.org:liu-90723DiVA: diva2:614373
ISBA Regional Meeting and International Workshop/Conference on Bayesian Theory and Applications (IWCBTA), 6-10 January 2013, Varanasi, Uttar Pradesh, India
FundereLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsSwedish Research Council