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This can be achieved by Querying under the Playback tab:
Set a Track Query (for any classification).
In the Results Panel, the query results will be tabled. One column is labelled 'History'.
Select the rewind icon in the History column for the track you wish to analyse.
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A Track Details window will appear.
Select the Classification tab.
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A table of results will appear, listing the track's classification probabilities. These are calculated as a fraction of achieved points out of total points possible e.g. 10500/17000
In the above table, the track has been classified as a Vehicle, but has in an equal probability (70.6%) of being classified as a Large Vehicle.
These results merit classification adjustment, in order to differentiate between Vehicle classification and Large Vehicle classification. By modifying the classification, you can make a new set of point values, alongside the original score, for comparison.
Classification Adjustment
Firstly, analyse the individual cell results by hovering over them with the mouse pointer. Small boxes of information regarding the individual values of the different properties will appear.
In this example, the Weight classification has been highlighted. Note that the Value has fallen below the Vehicle classification Weight range (45-65>31.88), and thus the track is smaller and therefore not a Large Vehicle.
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Pull the Track Details window to one side, and select Classification Configs. Ensure the Config Function is enabled.
Select Edit, and then under Classification Models, adjust whichever value you see fit. In this example, the Vehicle: Weight and Strength: Weight range has been adjusted to 30-65.00.
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When you select Save, a new tab will appear on the Track Details window: Modified Classification.
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A change in the Weight range has changed the score of the Vehicle classification, and raised the probability of the track being a Vehicle to 76.5%, clearly differentiating it from a Large Vehicle.
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There may be situations where a track doesn’t score highly at all in any model category. Regardless this track would still be classified according to the one with the highest score. To prevent this, the user is able to configure a percentage confidence rate to which the classification should aim, in the Advanced tab under the Configure Rules; https://navtechradar.atlassian.net/wiki/spaces/TUN/pages/211321151/ClearWay+Rules#Configuring-a-Rule window. You may set this to be very strict, however a satisfactory rate may be 70% |
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It may be necessary to create a new Classification Config for a system as the Default one is not editable. A new one can have it’s models settings tweaked and it is also possible to add / remove models.
To do this click, on Classification Configs in the Configuration Tree and select New:
Then select Classification Config:
This will open a Create New Classification Configuration wizard:
Give your new Classification Config an appropriate name and click Finish:
This will create a new Classification Config for you to modify. The Classification Models that were in the Default Classification Config have been replicated here, to allow you to tweak the settings, add or remove a model.
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To edit a model, select it and navigate through the settings and amend the ones required:
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By default, the Animal setting has not been enabled, so to enable it select the Enabled option:
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To add another model, if it is similar to another in size and weight, it will be easier to clone one of the current models by clicking the clone icon:
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Then select another Classification from the drop-down list:
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This will then display the new model:
The settings can be adjusted to suit the new model:
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To remove a model from the list, click on it and then select the trash icon:
Once you are happy with your modifications, click Save:
The new Classification Config will now be displayed in the Configuration Tree:
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Applying a Classification Configuration
To change the Classification Configs from the Default to another:
Go to Topology, select the track engine and click Edit:
Select the desired Classification Config from the drop-down list:
Then click Save:
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