Safe Haskell  None 

Language  Haskell2010 
This module implements the Extended Kalman Filter estimation algorithm.
 data KalmanFilter state var = KalmanFilter {
 kalmanState :: state var
 kalmanCovariance :: state (state var)
 data KalmanInnovation obs var = KalmanInnovation {
 kalmanInnovation :: obs var
 kalmanInnovationCovariance :: obs (obs var)
 newtype EKFProcess state var = EKFProcess (forall s. Reifies s Tape => state (Reverse s var) > state (Reverse s var))
 newtype EKFMeasurement state var = EKFMeasurement (forall s. Reifies s Tape => state (Reverse s var) > Reverse s var)
Documentation
data KalmanFilter state var Source #
All variants of Kalman Filter, at their core, maintain the parameters of a multivariate normal distribution.
Since different Kalman Filter variants share this filter type, you can mix and match algorithms within the same filter. For example, you could use a conventional Kalman filter for any linear measurements, and a SigmaPoint Kalman Filter for a nonlinear process model.
KalmanFilter  

GaussianFilter KalmanFilter Source #  
type Var (KalmanFilter state var) Source #  
type State (KalmanFilter state var) Source #  
data KalmanInnovation obs var Source #
Kalman filter estimators can report the innovation of each observation, as well as the covariance of the innovation.
KalmanInnovation  

newtype EKFProcess state var Source #
A process model in an Extended Kalman Filter transforms a state vector to a new state vector, but is wrapped in reversemode automatic differentiation.
EKFProcess (forall s. Reifies s Tape => state (Reverse s var) > state (Reverse s var)) 
(Additive state, Traversable state, Distributive state, Num var) => Process (EKFProcess state var) Source #  
Estimator (EKFProcess state var) Source #  
type Var (EKFProcess state var) Source #  
type State (EKFProcess state var) Source #  
type Filter (EKFProcess state var) Source #  
newtype EKFMeasurement state var Source #
A measurement model in an Extended Kalman Filter uses the state vector to predict what value a sensor should return, while wrapped in reversemode automatic differentiation.
EKFMeasurement (forall s. Reifies s Tape => state (Reverse s var) > Reverse s var) 
(Additive state, Distributive state, Traversable state, Fractional var) => Measure (EKFMeasurement state var) Source #  
Estimator (EKFMeasurement state var) Source #  
type Var (EKFMeasurement state var) Source #  
type State (EKFMeasurement state var) Source #  
type Filter (EKFMeasurement state var) Source #  
type MeasureQuality (EKFMeasurement state var) obs Source #  
type MeasureObservable (EKFMeasurement state var) obs Source #  