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Predict state and state estimation error covariance of linear Kalman filter

`[`

returns the predicted state, `xpred`

,`Ppred`

] = predict(`filter`

)`xpred`

, and the predicted state
estimation error covariance, `Ppred`

, for the next time step
of the input linear Kalman filter. The predicted values overwrite the internal
state and state estimation error covariance of
`filter`

.

This syntax applies when you set the `ControlModel`

property of `filter`

to an empty matrix.

`[`

specifies the state transition model, `xpred`

,`Ppred`

] = predict(`filter`

,`F`

,`Q`

)`F`

, and the process
noise covariance, `Q`

. Use this syntax to change the state
transition model and process noise covariance during a simulation.

This syntax applies when you set the `ControlModel`

property of `filter`

to an empty matrix.

`[`

specifies the force or control input, `xpred`

,`Ppred`

] = predict(`filter`

,`u`

,`F`

,`G`

)`u`

, the state
transition model, `F`

, and the control model,
`G`

. Use this syntax to change the state transition model
and control model during a simulation.

This syntax applies when you set the `ControlModel`

property of `filter`

to a nonempty matrix.

`[`

specifies the force or control input, `xpred`

,`Ppred`

] = predict(`filter`

,`u`

,`F`

,`G`

,`Q`

)`u`

, the state
transition model, `F`

, the control model,
`G`

, and the process noise covariance,
`Q`

. Use this syntax to change the state transition
model, control model, and process noise covariance during a simulation.

This syntax applies when you set the `ControlModel`

property of `filter`

to a nonempty matrix.

`predict(`

updates `filter`

,___)`filter`

with the predicted state and state
estimation error covariance without returning the predicted values. Specify the
tracking filter and any of the input argument combinations from preceding
syntaxes.

`clone`

| `correct`

| `correctjpda`

| `distance`

| `initialize`

| `likelihood`

| `residual`