Adding an example showing an explicit MHT application using MFA components #954
Conversation
A-acuto
requested review from
orosoman-dstl and
csherman-dstl
and removed request for
a team
docs/examples/mht_example.py
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| tracks.add(track) | ||
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| plotter.plot_tracks(tracks, [0, 2], track_label="Tracks", line=dict(color="Green")) |
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Looking at this plot, one track is distracted by the clutter and flies off in a straight line towards the bottom left of the graph. Is this something you want to address, either by commenting on the algorithm's performance or by changing parameters to obtain a better result?
…ore references on the existing tutorials and examples
docs/examples/mht_example.py
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| # We have prepared all the general parameters for the simulation, | ||
| # including the clutter spatial density. In this example we set | ||
| # the birth rate and the death probability as zero, using only the knowledge of the | ||
| # prior states to generate the tracks so the number of targets is fixed (3 in this case). |
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Am I right in thinking that MFA (or this implementation) only works with fixed number of tracks?
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Hi @orosoman-dstl and @sdhiscocks : I have applied the suggested changes to the text and references. Regarding @sdhiscocks question about the number of tracks: I tried using the Happy to discuss a more general solution (if possible with the current setup). |
To be honest, I wasn't sure if there was a limitation in the actual algorithm itself that means it assumes fixed number of targets, or limitation of the implementation. I've found when trying to use with varying targets, you can get an |
docs/examples/mht_example.py
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| # include Global Nearest Neighbour (GNN, :doc:`check here <../tutorials/06_DataAssociation-MultiTargetTutorial>`), | ||
| # Joint Probabilistic Data association (JPDA, :doc:`tutorial here <../tutorials/08_JPDATutorial>`), |
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I think prefixing the tutorials directory with auto_ should work
| # include Global Nearest Neighbour (GNN, :doc:`check here <../tutorials/06_DataAssociation-MultiTargetTutorial>`), | |
| # Joint Probabilistic Data association (JPDA, :doc:`tutorial here <../tutorials/08_JPDATutorial>`), | |
| # include Global Nearest Neighbour (GNN, :doc:`check here <../auto_tutorials/06_DataAssociation-MultiTargetTutorial>`), | |
| # Joint Probabilistic Data association (JPDA, :doc:`tutorial here <../auto_tutorials/08_JPDATutorial>`), |
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Thanks! This looks like a good suggestion (hopefully it'll fix this)! I'll implement it as I have other changes to make from other reviews.
Fix links to other tutorials
This PR adds an example of a Multi-Hypothesis tracking using the Multi-frame assignment components.
This example was supposed to rely more on Stone Soup components (e.g.,
GaussianMixtureInitiatorandMultiTargetTracker), however the current implementation of such components is not aligned with the MFA components making complex to deal with it (e.g. #896).This example shows how to set up this type of simulations in a more general way than the example present in the documentation.