Simple Kalman Filter Simulated measurements [Linear]
Simple Kalman Filter:
-https://www.geeksforgeeks.org/python/kalman-filter-in-python/
-https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/07-Kalman-Filter-Math.ipynb
-https://stackoverflow.com/questions/66007351/kalman-filter-2d-with-pykalman
Extended and Unscented Kalman Filter:
-https://ieeexplore-ieee-org.tudelft.idm.oclc.org/stamp/stamp.jsp?tp=&arnumber=7528889
-https://groups.seas.harvard.edu/courses/cs281/papers/unscented.pdf
-https://www-sciencedirect-com.tudelft.idm.oclc.org/science/article/pii/S0959152407001655
Math behind the simple kalman filter:
-https://aleksandarhaber.com/introduction-to-kalman-filter-derivation-of-the-recursive-least-squares-method-with-python-codes/
-https://aleksandarhaber.com/time-propagation-of-state-vector-and-state-covariance-matrix-of-linear-dynamical-systems-intro-to-kalman-filtering/
-https://aleksandarhaber.com/kalman-filter-complete-derivation-from-scratch/
(G)OSPA distance:
-https://ieeexplore-ieee-org.tudelft.idm.oclc.org/stamp/stamp.jsp?tp=&arnumber=8009645
-https://ieeexplore-ieee-org.tudelft.idm.oclc.org/stamp/stamp.jsp?tp=&arnumber=5744132&tag=1
Stone soup documentation:
-https://stonesoup.readthedocs.io/en/v0.1b4/stonesoup.metricgenerator.ospametric.html
Interacting Multiple Model (IMM):
-https://secwww.jhuapl.edu/techdigest/Content/techdigest/pdf/V22-N04/22-04-Genovese.pdf
-https://www.mdpi.com/2076-3417/16/5/2377
Error covariance matrix P has to have a large trace! The initial certainty must be very low to adopt changes faster.
Added OSPA distance as an evaluation metric.