Add Joseph form of covariance calculation to Kalman Updater #1028
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csherman-dstl
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jswright-dstl and
akenyon
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #1028 +/- ##
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+ Coverage 93.58% 93.59% +0.01%
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Files 203 203
Lines 13115 13123 +8
Branches 2678 2679 +1
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+ Hits 12274 12283 +9
- Misses 591 592 +1
+ Partials 250 248 -2
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sdhiscocks
approved these changes
May 28, 2024
stonesoup/updater/kalman.py
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Comment on lines
218
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227
| return post_cov.view(CovarianceMatrix), kalman_gain | ||
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| else: | ||
| kalman_gain = hypothesis.measurement_prediction.cross_covar @ \ | ||
| np.linalg.inv(hypothesis.measurement_prediction.covar) | ||
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| post_cov = hypothesis.prediction.covar - kalman_gain @ \ | ||
| hypothesis.measurement_prediction.covar @ kalman_gain.T | ||
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| return post_cov.view(CovarianceMatrix), kalman_gain |
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Suggested change
| return post_cov.view(CovarianceMatrix), kalman_gain | |
| else: | |
| kalman_gain = hypothesis.measurement_prediction.cross_covar @ \ | |
| np.linalg.inv(hypothesis.measurement_prediction.covar) | |
| post_cov = hypothesis.prediction.covar - kalman_gain @ \ | |
| hypothesis.measurement_prediction.covar @ kalman_gain.T | |
| return post_cov.view(CovarianceMatrix), kalman_gain | |
| else: | |
| kalman_gain = hypothesis.measurement_prediction.cross_covar @ \ | |
| np.linalg.inv(hypothesis.measurement_prediction.covar) | |
| post_cov = hypothesis.prediction.covar - kalman_gain @ \ | |
| hypothesis.measurement_prediction.covar @ kalman_gain.T | |
| return post_cov.view(CovarianceMatrix), kalman_gain |
akenyon
approved these changes
May 29, 2024
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Add the implementation of Joseph form of covariance to the Kalman Updater (as implemented in the Bayesian Recursive Updater).
Fix error arising when using Joseph form of covariance in BRUF with no measurement model.
Add tests.