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Cubature Kalman filter for object tracking

The `trackingCKF`

object represents a cubature Kalman filter
designed for tracking objects that follow a nonlinear motion model or are measured by a
nonlinear measurement model. Use the filter to predict the future location of an object, to
reduce noise in a measured location, or to help associate multiple object detections with
their tracks.

The cubature Kalman filter estimates the uncertainty of the state and the propagation of
that uncertainty through the nonlinear state and measurement equations. There are a fixed
number of cubature points chosen based on the spherical-radial transformation to guarantee an
exact approximation of a Gaussian distribution up to the third moment. As a result, the
corresponding filter is the same as an unscented Kalman filter, `trackingUKF`

, with `Alpha`

= 1, `Beta`

= 0, and
`Kappa`

= 0.

`ckf = trackingCKF`

`ckf = trackingCKF(transitionFcn,measuremntFcn,state)`

`ckf = trackingCKF(___,Name,Value)`

returns a cubature
Kalman filter object with default state transition function, measurement function,
state, and additive noise model.`ckf`

= trackingCKF

specifies the `ckf`

= trackingCKF(transitionFcn,measuremntFcn,state)`StateTranstionFcn`

,
`MeasurementFcn`

, and `State`

properties
directly.

specifies the properties of the Kalman filter using one or more
`ckf`

= trackingCKF(___,Name,Value)`Name,Value`

pair arguments. Any unspecified properties take default
values.

`predict` | Predict state and state estimation error covariance |

`correct` | Correct state and state estimation error covariance |

`correctjpda` | Correct state and state estimation error covariance using JPDA |

`distance` | Distances between measurements and predicted measurements |

`residual` | Measurement residual and residual noise |

`likelihood` | Likelihood of measurement |

`clone` | Copy filter for object tracking |