# trackingABF

## Description

The `trackingABF`

object represents an alpha-beta filter designed
for object tracking for an object that follows a linear motion model and has a linear
measurement model. Linear motion is defined by constant velocity or constant acceleration. Use
the filter to predict the future location of an object, to reduce noise for a detected
location, or to help associate multiple objects with their tracks.

## Creation

### Description

returns an alpha-beta
filter for a discrete time, 2-D constant velocity system. The motion model is named
`abf`

= trackingABF`'2D Constant Velocity'`

with the state defined as ```
[x; vx;
y; vy]
```

.

specifies
the properties of the filter using one or more `abf`

= trackingABF(Name,Value)`Name,Value`

pair
arguments. Any unspecified properties take default values.

## Properties

## Object Functions

`predict` | Predict state and state estimation error covariance of tracking filter |

`correct` | Correct state and state estimation error covariance using tracking filter |

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

`distance` | Distances between current and predicted measurements of tracking filter |

`likelihood` | Likelihood of measurement from tracking filter |

`smooth` | Backward smooth state estimates of tracking filter |

`clone` | Create duplicate tracking filter |

## Examples

## References

[1] Blackman, Samuel S.
"*Multiple-target tracking with radar applications.*" Dedham, MA,
Artech House, Inc., 1986, 463 p. (1986).

[2] Bar-Shalom, Yaakov, X. Rong Li,
and Thiagalingam Kirubarajan. *Estimation with applications to tracking and
navigation: theory algorithms and software*. John Wiley & Sons,
2004.

## Extended Capabilities

## Version History

**Introduced in R2018b**