Matt, as I understand it, a SCAAT system uses incomplete information, but its pose estimate is "complete" at the end of each measurement update. Visualization in state space might be more appropriate here - standard tracking systems try to estimate closed-form solutions for points in state space, while SCAAT pushes the current state estimate along the "track" most consistent with the most recent observation.

Though, one needs to ensure that the measurements can complementarily retrieve pose.

It's true that the paper is largely silent on the effect of measurement errors due to significant target dynamics, in particular whether such errors would be more than those in standard EKF implementations.

But the possibility of such errors is less in a SCAAT system. SCAAT allows for autocalibration in each iteration, so the device (source or sensor) that is most frequently used is also the most well-calibrated. This has an effect on accuracy akin to Amdahl's law on speed, consequently, one-off measurement errors (or random noises) actually may not have a very large effect.