# estimateStates

## Syntax

## Description

returns the state estimates based on the motion model used in the filter, the sensor data,
and the measurement noise. The function predicts the filter state estimates forward in time
based on the row times in `estimates`

= estimateStates(`filter`

,`sensorData`

,`measurementNoise`

)`sensorData`

and fuses data from each column of
the table one by one.

`[`

additionally returns the smoothed state estimates by using the Rach-Tung-Striebel (RTS)
nonlinear Kalman smoother. For algorithm details, see Algorithms and [1].`estimates`

,`smoothEstimates`

] = estimateStates(___)

**Tip**

Smoothing usually requires considerably more memory and computation time. Use this syntax only when you need the smoothed estimated states.

## Examples

## Input Arguments

## Output Arguments

## Algorithms

## References

[1] Crassidis, John L., and John L. Junkins. "Optimal Estimation of Dynamic Systems". 2nd ed, CRC Press, pp. 349- 352, 2012.

## Extended Capabilities

## Version History

**Introduced in R2022a**

## See Also

`predict`

| `fuse`

| `residual`

| `correct`

| `stateparts`

| `statecovparts`

| `stateinfo`

| `tune`

| `createTunerCostTemplate`

| `tunerCostFcnParam`