3 ms·

Only if you can make a linear model of those things…

• bradly 2y ago
  Why does the model need to be linear?
  • pinkmuffinere 2y ago
    “Kalman filter” usually refers to “linear quadratic estimator”, which assumes a linear model in its derivation. This will impact the “predict“ step at the very least, and I think also the way the uncertainty propagates. There are nonlinear estimators as well, though they usually have less-nice guarantees (eg particle filter, extended kalman filter) Edit: in fact, I see part three of the book in tfa is devoted to nonlinear Kalman filters. I suspect some of the crowd (myself included) just assumed we were talking about linear Kalman filters
    • RossBencina 2y ago
      Indeed the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are both standard methods to deal with non-linear systems. They each provide a method of propagating uncertainty through nonlinear state and measurement functions. FWIW I would assume that EKF and UKF are within scope whenever Kalman Filtering is mentioned but I may be misinformed.
      • pinkmuffinere 2y ago
        It’s probably just my bias making me think of linear systems — I’ve only used the nonlinear ones in coursework, certainly less familiar. Apologies if I misrepresented it.
        • bradly 2y ago
          I appreciate both your initial comment and responses as it gave me a place to ask and learn more. I wasn't asking as an accusation–so I hope it didn't come off that way– but as someone not understanding what makes something work/not work in linear/non-liner systems.