Quick explanation of how a Kalman filter works
Nice summary. I think of the concept at a slightly higher level of abstraction:<p>It&#x27;s just Bayesian inference to update the posterior. A theoretical model gives an estimate of what the state of the system should be (call that the &quot;prior&quot;). A sensor measurement gives an independent estimate of the state (call that the &quot;likelihood&quot;). Composing (multiplying) the two gives the posterior. Further, in this formulation it doesn&#x27;t really matter what you call the prior or the posterior. You can easily combine estimates from many different sources&#x2F;sensors.<p>Kalman filters are just the special case where the likelihood and prior are both Gaussian -- the distributions can be specified with a couple of numbers, and there is a simple closed-form expression for composing them. More generally, one can use all the tools available for Bayesian inference.