Hi first post hoping for some advise or guidance. Let's say I have GPS data on a robot every 10 Hz (every 0.1 second), I have accelerometer data every 40 Hz and steering wheel data every 80 Hz. I would like to use Kalman filter to produce estimates of where the robot is every 40 Hz given the previous data (e.g. interpolate the GPS data using Kalman). My question is what methodology should I use (if anyone knows a good paper on the subject I would be very grateful). My current thoughts are to either: 1. Apply a Kalman filter to each data series separately to interpolate to the highest frequency (i.e. only have lagged values of Y as its state variable) and then again with the entire datasets in our filter. 2. Have my Y (left hand side) be a vector of size [8*1] containing the GPS where the column vector is 0 everywhere but at 10Hz, and then apply Kalman filter (accelerometer vector would be treated the same but with two observations and NaN everywhere else). Thanks a lot.