Implicit particle filters for data assimilation
For researchers in data assimilation, this work offers improved particle filtering methods with more general equation-solving techniques.
Implicit particle filters for data assimilation guide particles to high-probability regions by first choosing probabilities and then finding particle locations. The paper provides detailed descriptions, examples, new solution methods for algebraic equations, and a new parameter identification algorithm.
Implicit particle filters for data assimilation update the particles by first choosing probabilities and then looking for particle locations that assume them, guiding the particles one by one to the high probability domain. We provide a detailed description of these filters, with illustrative examples, together with new, more general, methods for solving the algebraic equations and with a new algorithm for parameter identification.