Dead reckoning is a form of prediction, based on past evidence that indicates location then, you are reckoning (best guessing) a current position and detrmining a direction to move forward to reach some target.
"Past evidence that indicates" is deliberate phrasing, in the majority of these examples we are looking at acquired data with noise; errors, instrument noise, missing returns, etc.
"Tracking" is multi-stage, there's a desired target to be found (or to be declared absent) in noisy data .. that's pattern search and locking, the trajectory (the track) of that target must be best guessed, and the best guess forward prediction can be used to assist the search for the target in a new position.
This is not all that can be done with a Kalman filter but it's typical of a class of common applications.
"Past evidence that indicates" is deliberate phrasing, in the majority of these examples we are looking at acquired data with noise; errors, instrument noise, missing returns, etc.
"Tracking" is multi-stage, there's a desired target to be found (or to be declared absent) in noisy data .. that's pattern search and locking, the trajectory (the track) of that target must be best guessed, and the best guess forward prediction can be used to assist the search for the target in a new position.
This is not all that can be done with a Kalman filter but it's typical of a class of common applications.