Getting started

Getting started


Use the built-in package manager:

using Pkg

Basic usage

To load the package, use the command:

using FeedbackParticleFilters

Set up a basic one-dimensional linear-Gaussian continuous-time filtering problem:

using Distributions
state_model = ScalarDiffusionStateModel(x->-x, x->sqrt(2.), Normal())
obs_model   = ScalarDiffusionObservationModel(x->x)

filt_prob = ContinuousTimeFilteringProblem(state_model, obs_model)

Once the filtering problem is defined, you can use it to perform a variety of tasks.

For example, you may initialize an ensemble of N=100 particles:

ensemble = UnweightedParticleEnsemble(state_model, 100)

The following generates a Poisson equation for the gain using the ensemble above. The equation is solved using the semigroup gain estimation method.

eq = GainEquation(state_model, obs_model, ensemble)
method = SemigroupMethod(1E-1,1E-2)
solve!(eq, method)

The gain at the particle locations is stored in eq.gain. These low-level building blocks can then be used to write custom numerical implementations. The package also comes with methods to automatically simulate a given filtering problem:

filter = FPF(filt_prob, method, 100)
simulation = ContinuousTimeSimulation(filt_prob, filter, 10000, 0.01)