**'org.apache.commons.math3.filter.KalmanFilter'**Java class

org.apache.commons.math3.filter

## Class KalmanFilter

- java.lang.Object
- org.apache.commons.math3.filter.KalmanFilter

public class KalmanFilterextends Object

Implementation of a Kalman filter to estimate the state*x*of a discrete-time controlled process that is governed by the linear stochastic difference equation:_{k}*x*=_{k}**A***x*+_{k-1}**B***u*+_{k-1}*w*_{k-1}*x*that is_{k}*z*=_{k}**H***x*+_{k}*v*._{k}The random variables

*w*and_{k}*v*represent the process and measurement noise and are assumed to be independent of each other and distributed with normal probability (white noise)._{k}The Kalman filter cycle involves the following steps:

- predict: project the current state estimate ahead in time
- correct: adjust the projected estimate by an actual measurement

The Kalman filter is initialized with a

`ProcessModel`

and a`MeasurementModel`

, which contain the corresponding transformation and noise covariance matrices. The parameter names used in the respective models correspond to the following names commonly used in the mathematical literature:- A - state transition matrix
- B - control input matrix
- H - measurement matrix
- Q - process noise covariance matrix
- R - measurement noise covariance matrix
- P - error covariance matrix

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