Local and remote track file registration using minimum description length

A blue force platform (own-ship) contains a sensor suite from which a local track file is developed. In addition, using uplinked information from other blue sensors, own-ship develops a remote track file that represents red forces tracked by blue. To determine if own-ship has been inadvertently targeted by friendly forces, it requires the probability that it is in the remote track file and an estimate of the grid reference. 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. The situation in which the likelihood is maximized when all tracks correspond to distinct objects 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