Non-Bayesian particle filters
This work addresses the computational bottleneck of particle filters for nonlinear data assimilation, offering a potentially more efficient alternative for practitioners.
The authors propose a non-Bayesian particle filter that directly samples probability density functions via iteration, reducing the computational cost of traditional Bayesian particle filters. They demonstrate the method on a detailed example, showing improved efficiency.
Particle filters for data assimilation in nonlinear problems use "particles" (replicas of the underlying system) to generate a sequence of probability density functions (pdfs) through a Bayesian process. This can be expensive because a significant number of particles has to be used to maintain accuracy. We offer here an alternative, in which the relevant pdfs are sampled directly by an iteration. An example is discussed in detail.