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/KalmanandBayesianFiltersinPython
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 MultipleModel (IMM) estimator.
References
BarShalom, Y., Li, XR., and Kirubarajan, T. “Estimation with Application to Tracking and Navigation”. WileyInterscience, 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/KalmanandBayesianFiltersinPython
Examples
See my book Kalman and Bayesian Filters in Python https://github.com/rlabbe/KalmanandBayesianFiltersinPython

__init__
(filters, mu, M)[source]¶ ” Create an IMM estimator from a list of filters.
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.
