A blue force platform (own-ship) contains a sensor suite from which a local track file is developed. In addition, using side information from other blue sensors, own-ship develops a remote track file that represents blue forces tracked by red. The origin of the remote track file in the local reference frame (grid reference) is not known by own-ship. To determine if own-ship has been targeted by red forces, own-ship requires the probability that it is in the remote track file. In addition, an estimate of the grid reference is required. The tracks are assumed to consist of a sequence of independent measurements with Gaussian errors. The likelihood function for the local and remote tracks conditioned on the actual object trajectories, grid reference, number of objects and the association between objects and tracks is derived. Unfortunately, the likelihood function is independent of the number of objects, which leads to a situation where the likelihood is maximized when all tracks correspond to distinct objects. This situation is avoided by using the minimum description length (MDL) principle, which includes a term that penalizes an overparameterization of the model. Using MDL, an algorithm is presented for estimating the grid reference and for computing the probability that own-ship is tracked by blue forces. A Monte Carlo performance analysis of the algorithm is presented.