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Create Kalman filter for object tracking

- example
`kalmanFilter = configureKalmanFilter(MotionModel,InitialLocation,InitialEstimateError,MotionNoise,MeasurementNoise)`

returns a `kalmanFilter`

= configureKalmanFilter(`MotionModel`

,`InitialLocation`

,`InitialEstimateError`

,`MotionNoise`

,`MeasurementNoise`

)`vision.KalmanFilter`

object
configured to track a physical object. This object moves with constant
velocity or constant acceleration in an *M*-dimensional
Cartesian space. The function determines the number of dimensions, *M*,
from the length of the `InitialLocation`

vector.

This function provides a simple approach for configuring the `vision.KalmanFilter`

object
for tracking a physical object in a Cartesian coordinate system. The
tracked object may move with either constant velocity or constant
acceleration. The statistics are the same along all dimensions. If
you need to configure a Kalman filter with different assumptions,
use the `vision.KalmanFilter`

object
directly.

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