Gaussian-sum filter for object tracking

The `trackingGSF`

object represents a Gaussian-sum filter designed
for object tracking. You can define the state probability density function by a set of finite
Gaussian-sum components. Use this filter for tracking objects that require a multi-model
description due to incomplete observability of state through measurements. For example, this
filter can be used as a range-parameterized extended Kalman filter when the detection contains
only angle measurements.

returns a Gaussian-sum
filter with two constant velocity extended Kalman filters (`gsf`

= trackingGSF`trackingEKF`

) with equal initial weight.

specifies the Gaussian components of the filter in `gsf`

= trackingGSF(`trackingFilters`

)`trackingFilters`

.
The initial weights of the filters are assumed to be equal.

specifies the initial weight of the Gaussian components in
`gsf`

= trackingGSF(`trackingFilters`

,`modelProbabilities`

)`modelProbabilities`

and sets the
`ModelProbabilities`

property.

specifies the measurement noise of the filter. The `gsf`

= trackingGSF(___,'MeasurementNoise',measNoise)`MeasurementNoise`

property is set for each Gaussian component.

`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 |

`clone` | Create duplicate tracking filter |

[1] Alspach, Daniel, and Harold
Sorenson. "Nonlinear Bayesian estimation using Gaussian sum approximations." *IEEE
Transactions on Automatic Control.* Vol. 17, No. 4, 1972, pp.
439–448.

`trackingCKF`

| `trackingEKF`

| `trackingMSCEKF`

| `trackingPF`

| `trackingUKF`