I am trying to implement an EKF to estimate my position and velocity states by using accelerometer measurements as well as periodic GPS (position) measurements. Basically I want to use the constantly use the accelerometer and re-calibrate it for every position measurement that is available. When there is GPS dropouts, the estimates rely solely on the accelerometer readings.
I believe I have it working if all measurements are available. Since my acceleration is not one of my states, my accelerometer measurements are used in the predict side of my filter. The position measurements are in the update side like normal.
If a position measurement is unavailable, what do I do during my update? Without a position measurement, am I basically just saying that a priori estimate is equal to my posteriori? (i.e. my "pretend" position measurement is just my estimated position).
Or is there something fancier that has to be done? Is there issues with the system not being "observable" (i.e. observability matrix not full rank) when the position measurements are not available?
Thanks in advance!