IMM Estimator

needs documentation....


Copyright 2015 Roger R Labbe Jr.

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

Documentation at: https://filterpy.readthedocs.org

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

This is licensed under an MIT license. See the readme.MD file for more information.

class filterpy.kalman.IMMEstimator(filters, mu, M)[source]

Implements an Interacting Multiple-Model (IMM) estimator.

References

Bar-Shalom, Y., Li, X-R., and Kirubarajan, T. “Estimation with Application to Tracking and Navigation”. Wiley-Interscience, 2001.

Crassidis, J and Junkins, J. “Optimal Estimation of Dynamic Systems”. CRC Press, second edition. 2012.

Labbe, R. “Kalman and Bayesian Filters in Python”. https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

__init__(filters, mu, M)[source]

” Parameters ———-

filters : (N,) array_like of KalmanFilter objects
List of N filters. filters[i] is the ith Kalman filter in the IMM estimator.
mu : (N,) ndarray of float
mode probability: mu[i] is the probability that filter i is the correct one.
M : (N,N) ndarray of float
Markov chain transition matrix. M[i,j] is the probability of switching from filter j to filter i.
update(z, u=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.

u : np.array, optional

u[i] contains the control input for the ith filter