This package provides some simple generic particle filters, and may serve as a template for making custom particle filters and other belief updaters. It is compatible with POMDPs.jl, but does not have to be used with that package.

In Julia:

Pkg.add("ParticleFilters")

Basic setup might look like this:

using ParticleFilters, Distributions dynamics(x, u, rng) = x + u + randn(rng) y_likelihood(x_previous, u, x, y) = pdf(Normal(), y - x) model = ParticleFilterModel{Float64}(dynamics, y_likelihood) pf = BootstrapFilter(model, 10)

Then the update function can be used to perform a particle filter update.

b = ParticleCollection([1.0, 2.0, 3.0, 4.0]) u = 1.0 y = 3.0 b_new = update(pf, b, u, y)

This is a very simple example and the framework can accommodate a variety of more complex use cases. More details can be found in the documentation linked to below.

There are tutorials for three ways to use the particle filters:

1. As an estimator for feedback control,
2. to filter time-series measurements, and
3. as an updater for POMDPs.jl.

https://JuliaPOMDP.github.io/ParticleFilters.jl/latest