In tracking algorithms where measurements from various sensors are combined the track state representation is usually dependent on the type of sensor information that is received. When a multi-hypothesis tracking algorithm is used the probabilities of the different hypotheses containing tracks in different representations need to be re-evaluated when track state representations are changed. For the particular case of trilateration a method is presented to adapt the state representation as more information becomes available. A discussion is given on how to re-evaluate the probabilities of the hypotheses leading to a method for the trilateration case. This is illustrated by a simple example.