Timeline for In what sense is the Kalman filter optimal?
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Oct 5, 2021 at 7:08 | comment | added | SoftSail | In link, the optimality of the Kalman innovation gain is interpreted in the sense that it minimizes the trace of the a posteriori estimate covariance matrix | |
| Oct 4, 2021 at 16:30 | comment | added | TimWescott | It's a known thing that if the optimality criterion is to minimize the MSE error, then the Kalman filter is the best linear filter, for measurement and process noise that are zero-mean but otherwise any probability distribution. With emphasis on the MSE and linear -- there are some PDFs (i.e., strongly bimodal) where the least-MSE error may be the worst possible estimate in real life (e.g., the optimal path if you're driving straight toward an obstacle is either the left or right -- if you split the difference you'll be sorry). | |
| Oct 4, 2021 at 16:26 | comment | added | TimWescott | Now I want to know where I can find out what they mean by "smallest" in terms of matrix theory. | |
| Oct 4, 2021 at 15:11 | history | answered | Peter K.♦ | CC BY-SA 4.0 |