# EnsembleKalmanFilter¶

## Introduction and Overview¶

This implements the Ensemble Kalman filter.

Copyright 2015 Roger R Labbe Jr.

FilterPy library. http://github.com/rlabbe/filterpy

Supporting book at: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

class filterpy.kalman.EnsembleKalmanFilter(x, P, dim_z, dt, N, hx, fx)[source]

This implements the ensemble Kalman filter (EnKF). The EnKF uses an ensemble of hundreds to thousands of state vectors that are randomly sampled around the estimate, and adds perturbations at each update and predict step. It is useful for extremely large systems such as found in hydrophysics. As such, this class is admittedly a toy as it is far too slow with large N.

There are many versions of this sort of this filter. This formulation is due to Crassidis and Junkins . It works with both linear and nonlinear systems.

Parameters: x : np.array(dim_x) state mean P : np.array((dim_x, dim_x)) covariance of the state dim_z : int Number of of measurement inputs. For example, if the sensor provides you with position in (x,y), dim_z would be 2. dt : float time step in seconds N : int number of sigma points (ensembles). Must be greater than 1. K : np.array Kalman gain hx : function hx(x) Measurement function. May be linear or nonlinear - converts state x into a measurement. Return must be an np.array of the same dimensionality as the measurement vector. fx : function fx(x, dt) State transition function. May be linear or nonlinear. Projects state x into the next time period. Returns the projected state x.

References

•  John L Crassidis and John L. Junkins. “Optimal Estimation of Dynamic Systems. CRC Press, second edition. 2012. pp, 257-9.

Examples

def hx(x):
return np.array([x])

F = np.array([[1., 1.],
[0., 1.]])
def fx(x, dt):
return np.dot(F, x)

x = np.array([0., 1.])
P = np.eye(2) * 100.
dt = 0.1
f = EnKF(x=x, P=P, dim_z=1, dt=dt, N=8,
hx=hx, fx=fx)

std_noise = 3.
f.R *= std_noise**2
f.Q = Q_discrete_white_noise(2, dt, .01)

while True:
f.predict()
f.update(np.asarray([z]))


See my book Kalman and Bayesian Filters in Python https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

Attributes: x : numpy.array(dim_x, 1) State estimate P : numpy.array(dim_x, dim_x) State covariance matrix x_prior : numpy.array(dim_x, 1) Prior (predicted) state estimate. The *_prior and *_post attributes are for convienence; they store the prior and posterior of the current epoch. Read Only. P_prior : numpy.array(dim_x, dim_x) Prior (predicted) state covariance matrix. Read Only. x_post : numpy.array(dim_x, 1) Posterior (updated) state estimate. Read Only. P_post : numpy.array(dim_x, dim_x) Posterior (updated) state covariance matrix. Read Only. z : numpy.array Last measurement used in update(). Read only. R : numpy.array(dim_z, dim_z) Measurement noise matrix Q : numpy.array(dim_x, dim_x) Process noise matrix fx : callable (x, dt) State transition function hx : callable (x) Measurement function. Convert state x into a measurement K : numpy.array(dim_x, dim_z) Kalman gain of the update step. Read only. inv : function, default numpy.linalg.inv If you prefer another inverse function, such as the Moore-Penrose pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv
__init__(x, P, dim_z, dt, N, hx, fx)[source]

x.__init__(…) initializes x; see help(type(x)) for signature

initialize(x, P)[source]

Initializes the filter with the specified mean and covariance. Only need to call this if you are using the filter to filter more than one set of data; this is called by __init__

Parameters: x : np.array(dim_z) state mean P : np.array((dim_x, dim_x)) covariance of the state
update(z, R=None)[source]

Add a new measurement (z) to the kalman filter. If z is None, nothing is changed.

Parameters: z : np.array measurement for this update. R : np.array, scalar, or None Optionally provide R to override the measurement noise for this one call, otherwise self.R will be used.
predict()[source]

Predict next position.