Adding an example showing inverse-time dynamics method to deal with OOSM #940
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
A-acuto
requested review from
hpritchett-dstl and
csherman-dstl
and removed request for
a team
spike-dstl
requested changes
Feb 19, 2024
Comment on lines
16
to
21
| # multiple approaches on how to deal with OOS measurements, | ||
| # spanning from simply ignoring them, assuming that the fraction | ||
| # of these is small and it will not, significally, impact the quality of the | ||
| # tracking, iterate over last $\math{\ell}$ measurements with | ||
| # a fixed lag (see what it is called Algorithm A and B in [#]_ and | ||
| # [#]_, see also the previous examples) or, like in this example, |
There was a problem hiding this comment.
Suggested change
| # multiple approaches on how to deal with OOS measurements, | |
| # spanning from simply ignoring them, assuming that the fraction | |
| # of these is small and it will not, significally, impact the quality of the | |
| # tracking, iterate over last $\math{\ell}$ measurements with | |
| # a fixed lag (see what it is called Algorithm A and B in [#]_ and | |
| # [#]_, see also the previous examples) or, like in this example, | |
| # multiple approaches on how to deal with OOS measurements. | |
| # These approaches include simply ignoring the presence of | |
| # OOSM (assuming that the fraction of OOSM is small and it will | |
| # not significantly impact the tracking performance). Another | |
| # approach iterates over last $\math{\ell}$ measurements with | |
| # a fixed lag (see what it is called Algorithm A and B in [#]_ and | |
| # [#]_ (see the previous examples). In this example, |
There was a problem hiding this comment.
Please fill in the reference numbers in the text above
| # you can include any OOSM and re-process the measurement using | ||
| # inverse-time dynamics creating pseudo-measurements. | ||
| # This approach is called in literature as Algorithm C | ||
| # (from [#]_, [#]_) and it will be refered as such later on. |
Comment on lines
26
to
27
| # To explain how does it work let's consider a single target scenario, | ||
| # this algorithm deals with the presence of delayed measurements by |
There was a problem hiding this comment.
Suggested change
| # To explain how does it work let's consider a single target scenario, | |
| # this algorithm deals with the presence of delayed measurements by | |
| # To explain how it works, we consider a single target scenario. | |
| # This algorithm deals with the presence of delayed measurements by |
| # and with some level of clutter, at specific timesteps we insert a | ||
| # delay in one of the sensor scan and we employ Algorithm C to | ||
| # process in the correct way the chain of measurements. | ||
| # To visualise the benefit of the algorithm we, also, include a |
There was a problem hiding this comment.
Suggested change
| # To visualise the benefit of the algorithm we, also, include a | |
| # To visualise the benefit of the algorithm, we also include a |
| # ------------------------------------------------------------------- | ||
| # We can now run the tracker and generate the tracks. | ||
| # By looping over the scans we can spot any OOS measurement and apply the algorithm. | ||
| # When we encounter a delayed measurement, at time $\math{\tau}$, we make use of both |
There was a problem hiding this comment.
Suggested change
| # When we encounter a delayed measurement, at time $\math{\tau}$, we make use of both | |
| # When we encounter a delayed measurement, at time :math:`\tau`, we make use of both |
Comment on lines
251
to
252
| # target, then we use the transition model with time-inverse dynamics ($t_{k} - \math{\tau}$) | ||
| # to trace back where the target was in the scan-timestamp ($t$). With this pseudo-location, which is an |
There was a problem hiding this comment.
Suggested change
| # target, then we use the transition model with time-inverse dynamics ($t_{k} - \math{\tau}$) | |
| # to trace back where the target was in the scan-timestamp ($t$). With this pseudo-location, which is an | |
| # target, then we use the transition model with time-inverse dynamics (:math:`t_{k} - \tau`) | |
| # to trace back where the target was in the scan-timestamp :math:`t`. With this pseudo-location, which is an |
| # inverse function at $\math{\tau}$ to obtain a predicted Cartesian location of the | ||
| # target, then we use the transition model with time-inverse dynamics ($t_{k} - \math{\tau}$) | ||
| # to trace back where the target was in the scan-timestamp ($t$). With this pseudo-location, which is an | ||
| # approximation, we can compute the pseudo-measurement usign the measurement model. |
There was a problem hiding this comment.
Suggested change
| # approximation, we can compute the pseudo-measurement usign the measurement model. | |
| # approximation, we can compute the pseudo-measurement using the measurement model. |
| # target, then we use the transition model with time-inverse dynamics ($t_{k} - \math{\tau}$) | ||
| # to trace back where the target was in the scan-timestamp ($t$). With this pseudo-location, which is an | ||
| # approximation, we can compute the pseudo-measurement usign the measurement model. | ||
| # In this way now we have a pseudo-measurement at $t$ and we can keep the time-chain and |
There was a problem hiding this comment.
Suggested change
| # In this way now we have a pseudo-measurement at $t$ and we can keep the time-chain and | |
| # In this way now we have a pseudo-measurement at :math:`t` and we can keep the time-chain and |
| # ------------------------------------------------------------------------ | ||
| # We have obtained the final tracks from the detections and we have two sets of | ||
| # tracks, one with OOSM and one without. We now can visualise the results | ||
| # and evaluate the track-to-truh accuracy using the OSPA metric tool. |
There was a problem hiding this comment.
Suggested change
| # and evaluate the track-to-truh accuracy using the OSPA metric tool. | |
| # and evaluate the track-to-track accuracy using the OSPA metric tool. |
spike-dstl
approved these changes
Feb 27, 2024
nperree-dstl
requested changes
Apr 5, 2024
Comment on lines
+338
to
+347
| from stonesoup.plotter import AnimatedPlotterly | ||
| plotter = AnimatedPlotterly(timesteps=timestamps) | ||
| plotter.plot_ground_truths(truths, [0, 2]) | ||
| plotter.plot_measurements(scan_s1, [0, 2], measurements_label='scan1', measurement_model=sensor_1_mm) | ||
| plotter.plot_measurements(scan_s2, [0, 2], measurements_label='scan2', measurement_model=sensor_1_mm) | ||
| plotter.plot_tracks(oosm_tracks, [0, 2], track_label='OOSM Tracks', | ||
| line= dict(color='orange')) | ||
| plotter.plot_tracks(noOsm_tracks, [0, 2], track_label='no-OOSM Tracks', | ||
| line= dict(color='red')) | ||
| plotter.fig |
There was a problem hiding this comment.
The TrueDetections plotted in your output (blue points) look pretty noisy and a long way from the ground truth towards the end of the simulation. Ignore me if that's intentional, but might be worth adjusting the noise in your measurement model if not.
nperree-dstl
approved these changes
Apr 8, 2024
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Thir PR adds an example that shows how to implement Algorithm C [1, 2] to deal with out of sequence measurements.
As other examples (see PR #939 and PR #915 ) we explain how to treat OOSM, in this specific case we employ a time-inverse dynamics method to create pseudo-measurements at "correct timestamp" from the delayed detections.
[1] Y. Bar-Shalom, M. Mallick, H. Chen, R. Washburn, 2002, One-step solution for the general out-of-sequence measurement problem in tracking, Proceedings of the 2002 IEEE Aerospace Conference.
[2] S. R. Maskell, R. G. Everitt, R. Wright, M. Briers, 2005, Multi-target out-of-sequence data association: Tracking using graphical models, Information Fusion.