Adaptive kernel Kalman filter (AKKF) #1014
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jswright-dstl
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orosoman-dstl
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sdhiscocks
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May 16, 2024
stonesoup/types/state.py
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| @property | ||
| def mean(self): | ||
| return self.state_vector @ self.weight | ||
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| @property | ||
| def covar(self): | ||
| return self.state_vector @ self.kernel_covar @ self.state_vector.T |
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| # %% | ||
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| plotter.plot_tracks(track, [0, 2], particle=True) | ||
| plotter.fig |
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Adding the comment below should use last image for gallery thumbnail, which I think is better.
Suggested change
| # %% | |
| plotter.plot_tracks(track, [0, 2], particle=True) | |
| plotter.fig | |
| # %% | |
| # sphinx_gallery_thumbnail_number = -1 | |
| plotter.plot_tracks(track, [0, 2], track_label='AKKF - quadratic', color='royalblue', particle=True) | |
| plotter.fig |
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #1014 +/- ##
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+ Coverage 93.60% 93.66% +0.06%
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Files 202 205 +3
Lines 12990 13125 +135
Branches 2651 2668 +17
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+ Hits 12159 12294 +135
Misses 588 588
Partials 243 243
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This PR adds the Adaptive kernel Kalman filter (AKKF) [1]. The implementation includes the following:
QuadraticKernelQuarticKernelGaussianKernelAdaptiveKernelKalmanPredictorAdaptiveKernelKalmanUpdaterKernelParticleStateThe above implementations are described in [2].
[1] M. Sun, M. E. Davies, I. Proudler and J. R. Hopgood, "Adaptive Kernel Kalman Filter," 2021 Sensor Signal Processing for Defence Conference (SSPD), Edinburgh, United Kingdom, 2021, pp. 1-5, doi: 10.1109/SSPD51364.2021.9541455.
[2] J. S. Wright, J. R. Hopgood, M. E. Davies, I. K. Proudler and M. Sun, "Implementation of Adaptive Kernel Kalman Filter in Stone Soup," 2023 Sensor Signal Processing for Defence Conference (SSPD), Edinburgh, United Kingdom, 2023, pp. 1-5, doi: 10.1109/SSPD57945.2023.10256739.