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Particle filter for object tracking

The `trackingPF`

object represents an object tracker that follows a
nonlinear motion model or that is measured by a nonlinear measurement model. The filter uses a
set of discrete particles to approximate the posterior distribution of the state. The particle
filter can be applied to arbitrary nonlinear system models. The process and measurement noise
can follow an arbitrary non-Gaussian distribution.

The particles are generated using various resampling methods defined by
`ResamplingMethod`

.

returns a
`pf`

= trackingPF`trackingPF`

object with state transition function,
`@constvel`

, measurement function, `@cvmeas`

, and a
distribution of particles around the state, `[0;0;0;0]`

, with unit
covariance in each dimension. The filter assumes an additive Gaussian process noise
model and Gaussian likelihood calculations.

specifies the `pf`

= trackingPF(transitionFcn,measuremntFcn,state)`StateTransitionFcn`

,
`MeasurementFcn`

, and `State`

properties
directly. The filter assumes a unit covariance around the state.

specifies the properties of the particle filter using one or more
`pf`

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

pair arguments. Any unspecified properties take default
values.

`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 |

`initialize` | Initialize state and covariance of tracking filter |

[1] Arulampalam, M.S., S. Maskell, N.
Gordon, and T. Clapp. "A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian
Bayesian Tracking." *IEEE Transactions on Signal Processing.* Vol. 50,
No. 2, Feb 2002, pp. 174-188.

[2] Chen, Z. "Bayesian Filtering: From
Kalman Filters to Particle Filters, and Beyond." *Statistics*. Vol. 182,
No. 1, 2003, pp. 1-69.

`trackingKF`

| `trackingEKF`

| `trackingUKF`

| `trackingCKF`

| `cvmeas`

| `constvel`