Note: This page has been translated by MathWorks. Click here to see

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Partition detections based on Mahalanobis distance

Using multiple distance thresholds, the function separates detections into different detection cells based on their relative Mahalanobis distances and reports all the possible partitions. A partition of a set of detections is defined as a division of these detections into nonempty mutually exclusive detection cells. A detection cell is a group of detections whose distance to at least one other detection in the cell is less than the distance threshold. In other words, two detections belong to the same detection cell if their distance is less than the distance threshold.

`partitions = partitionDetections(detections)`

`partitions = partitionDetections(detections,tLower,tUpper)`

`partitions = partitionDetections(detections,tLower,tUpper,'MaxNumPartitions',maxNumber)`

`partitions = partitionDetections(detections,allThresholds)`

returns possible `partitions`

= partitionDetections(`detections`

)`partitions`

from `detections`

, using
distance partitioning algorithm. By default, the function considers all real value
Mahalanobis distance thresholds between 0.5 and 6.25.

allows you to specify the lower and upper bounds of the distance thresholds,
`partitions`

= partitionDetections(`detections`

,`tLower`

,`tUpper`

)`tLower`

and `tUpper`

.

allows you to specify the maximum number of allowed partitions,
`partitions`

= partitionDetections(`detections`

,`tLower`

,`tUpper`

,'MaxNumPartitions',`maxNumber`

)`maxNumber`

, in addition to the lower and upper bounds of the distance
thresholds, `tLower`

and `tUpper`

.

allows you to specify the exact thresholds considered for partition.`partitions`

= partitionDetections(`detections`

,`allThresholds`

)

[1] Granstorm, K., C. Lundquiest, and
O. Orguner. " Extended target tracking using a Gaussian-mixture PHD filter." * IEEE
Transactions on Aerospace and Electronic Systems.* Vol. 48, Number 4, 2012, pp.
3268–3286.