Adding an example comparing Particle filters and kalman filters using track fusion #875
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A-acuto
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| track_fusion_tracker.detector = Tracks2GaussianDetectionFeeder(dummy_detector_KF) | ||
| track_fusion_tracker.__iter__() | ||
| _, tracks = next(track_fusion_tracker) | ||
| KF_fused_track.update(tracks) |
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This line is causing the first cell at the start of Section "3) Run the trackers..." to fail on build with the following errors;
On CircleCI "ValueError: Mime type rendering requires ipython but it is not installed"
On local build "ValueError: All detections must have the same timestamp"
…he PF and the KF, added the change in the feeder tracking for track fusion, it works ok
… and example like
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nperree-dstl
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nperree-dstl
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nperree-dstl
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Apr 5, 2024
| plotter.fig | ||
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| # %% | ||
| # 2) Initialise the trackers components |
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Suggested change
| # 2) Initialise the trackers components | |
| # 2. Initialise the trackers components |
jswright-dstl
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Apr 17, 2024
sdhiscocks
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sdhiscocks
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This PR adds an example were we do tracking fusion using partial tracks obtained by different sensors and compares how an Extended Kalman filter results are similar or different to a Particle filter.
This PR also adds a solution to use
Tracks2GaussianDetectionFeederfor Particle States (and not only Gaussian states) as discussed and solved in #854I will add further examples (or enhance this one) regarding track fusion when other algorithms are presented (Covariance inflation)