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Covariance fusion using covariance intersection

`[`

fuses the track states in `fusedState`

,`fusedCov`

] = fusecovint(`trackState`

,`trackCov`

)`trackState`

and their corresponding covariance
matrices `trackCov`

. The function computes the fused state and covariance
as an intersection of the individual covariances. It creates a convex combination of the
covariances and finds weights that minimize the determinant of the fused covariance
matrix.

`[`

estimates the fused covariance by minimizing `fusedState`

,`fusedCov`

] = fusecovint(`trackState`

,`trackCov`

,`minProp`

)`minProp`

, which can be
either the determinant or the trace of the fused covariance matrix.

[1] Matzka, Stephan, and Richard
Altendorfer. "*A comparison of track-to-track fusion algorithms for automotive
sensor fusion.*" In Multisensor Fusion and Integration for Intelligent Systems,
pp. 69-81. Springer, Berlin, Heidelberg, 2009.

[2] Julier, Simon, and Jeffrey K.
Uhlmann. "*General decentralized data fusion with covariance
intersection.*" In Handbook of multisensor data fusion, pp. 339-364. CRC Press,
2017.