Mixing kinematics and identification data for track-to-track association

This paper presents a new track-to-track association algorithm mixing kinematics data from the radar and identification data from the ESM (Electronic Support Measure) sensor. In classical track-to-track association methods, only kinematics data from the radar are used. In this paper, we show how to improve the association using both kinds of information although they have different type. We also introduce a track identification algorithm in order to improve the performances of the method. Considering two tracks, the problem is formulated as the following hypothesis test: H-0 : both observed tracks are generated by the same target; H-1 : both observed tracks are generated by different targets. Then we compute a likelihood ratio mixing kinematics and identification data. The identification algorithm results are used to calculate the likelihood ratio. We compare it to a threshold. This technique enables to evaluate the performance of the algorithm in term of "probability of correct association" (probability of associating tracks when they correspond to the same target) and "probability of false association" (probability of associating tracks when they correspond to different targets). The threshold is chosen in order to constrain the probability of false association to a small value. This method, valid for any kind of track, can easily be generalized if the number of tracks is greater than two. It has the double advantage of providing information about the common origin of the tracks and an identification of each track.