Timeline for How to generate ground truth in constant velocity Kalman simulation?
Current License: CC BY-SA 4.0
4 events
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| Mar 28, 2023 at 15:30 | comment | added | TimWescott | An absolutely predictable system state evolution implies that $\mathbf Q$ is zero (assuming that the system model is 100% correct). But in general, yes. However, the Kalman filter assumes that you start your design knowing $\mathbf Q$ and $\mathbf R$. If you don't, then there are techniques for deducing them (or you can just guess) -- but getting them wrong can really mess up the filter performance. | |
| Feb 25, 2023 at 17:46 | comment | added | IMK | The only way I can think of in which they are correct is the following. The measurement is the only information we receive about the system. We need to decide what portion of the noise in these measurements is due to measurement noise and what portion is due to a process noise, so we allocate a covariance for each: one Q covariance, and one (typically named) R measurement covariance. And the Kalman gain gives us the optimal value to use for our Q & R assumption. That is to say: even if we have a constant velocity ground truth, Q need not be zero. Is that the correct understanding of the KF? | |
| Feb 25, 2023 at 17:39 | comment | added | IMK | Thanks for your answer Tim - appreciate your guidance on how to handle my prof's politically also. I'd be slightly surprised if they were wrong for two reasons: while both have filtering experience one is a world-renowned mathematician who is an expert in it; also they both immediately said that aspect was wrong and that the ground truth is never the noisy path. Most studies/implementations I find online talk about the equations, I have yet to come across any paper or code where I've found the ground truth generation for constant velocity (if you know any, please let me know). | |
| Feb 25, 2023 at 16:53 | history | answered | TimWescott | CC BY-SA 4.0 |