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The goal of this library is to simplify implementation and use of
state-space estimation algorithms, such as Kalman Filters. The
interface for constructing models is isolated as much as possible from
the specifics of a given algorithm, so swapping out a Kalman Filter
for a Bayesian Particle Filter should involve a minimum of effort.
This implementation is designed to support symbolic types, such as
from sbv or
ivory. As a result you can
generate code in another language, such as C, from a model written
using this package; or run static analyses on your model.
Also included is a sophisticated sensor fusion example in
Numeric.Estimator.Model.SensorFusion, which may be useful in its own
right.