Radar networks require techniques for information fusion, such as track-to-track fusion. However, existing fusion schemes in the literature seldom apply radar-like models, which can have profound effects on performance. Hence, this paper describes and analyses state of the art algorithms for track-to-track fusion assuming range-Doppler radar measurements. The algorithms considered are a central Kalman filter, Naive fusion and variants of the distributed Kalman filter. It is found that when the radar model is applied the distributed Kalman filter with globalised prediction is no longer exact, but feedback from the fusion centre or an approximation based on relaxed evolution can produce close to the optimal performance of a central Kalman filter. These variants of the distributed Kalman filter are preferred, as they do not require full communication. (6 pages)