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Issues in the description of the nearly-constant turn rate model #995

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narykov opened this issue Apr 25, 2024 · 2 comments
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Issues in the description of the nearly-constant turn rate model #995

narykov opened this issue Apr 25, 2024 · 2 comments
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@narykov
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narykov commented Apr 25, 2024

The description of the nearly-constant turn rate model in here doesn't look right.

image

The expression of $F(x)$ here is a vector, not a matrix as implied by $F$. An appropriate $F$ matrix can be seen, e.g., on p. 16 in here, but a bit struggling with finding a good reference. Regardless, in the entries to this vector above, it should be $x_{pos}$, not just $x$ (and the same, respectively, for $y$). Finally, a bit surprised to see $q_{\omega}^2$ not scaled by the time interval, which seems contradictory to the SDE in the model's description that states $d\omega = q_\omega dt$.

@sdhiscocks sdhiscocks added the bug label Apr 25, 2024
@narykov
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narykov commented Apr 25, 2024

OK, found a semi-decent conference paper with Bar-Shalom among the authors, which exposes the $F$ and $Q$ matrices. Indeed, the element in $Q$ that describes the turn rate noise is time-dependent, which means that the following is brought into question, i.e., q**2 may need to be turned into dt * q**2:

Q = np.array([[dt**3 / 3., dt**2 / 2.],
[dt**2 / 2., dt]])
C = block_diag(Q*q_x**2, Q*q_y**2, q**2)

P.S. It would be good to find reference for q**2, since in the publications I've come across either $q$ or $\sigma^2$, but never $q^2$.

@narykov
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narykov commented Apr 30, 2024

Just a small thing, there is no dependency on $x$ in $F(\cdot)$ below:
image
Thanks!

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