Kalman

Implements a generalized extended Kalman filter.

Makes heavy use of automatic differentiation to automatically linearize your arbitrary, possibly-nonlinear system model and sensor mappings.

Automatic differentiation is very fast, and computes derivatives to machine precision while avoiding finite differencing or symbolic manipulations. As long as all intermidiate computations are carried out using the Dual class, a sensible derivative will be computed.

The pieces are:

• A Sensor takes a state (made of Duals) and converts it to a reading, using arbitrary math.
• A Predictor takes a state (made of Duals) and a time delta, and produces a new state for t + dt, using arbitrary math.
• A KalmanFilter takes a Predictor and a list of Sensors, and constructs an EKF model for the system they describe.
• In an update loop, the KalmanFilter is fed Measurements (i.e. a sensor object and its reading) and dt, and updates itself to the new optimal estimate of state.

Dependencies

• Boost
• Geomc