The work presented here is pat of a generalization of Bayesian filtering and estimation theory to the problem of multisource, multitarget, multi-evidence unified joint detection, tracking, and target ID developed by Lockheed Martin Tactical Defense Systems and Scientific Systems Co., Inc. Our approach to robust joint target identification and tracking was to take the StaF algorithm and integrate it with a Bayesian nonlinear filter, where target position, velocity, pose, and type could then be determined simultaneously via maximum a posteriori estimation. The basis for the integration between the tracker and classifier is base don 'finite-set statistics' (FISST). The theoretical development of FISST is a Lockheed Martin ongoing project since 1994. The specific problem addressed in this paper is that of robust joint target identification and tracking via fusion of high range resolution radar (HRRR) - from the automatic radar target identification (ARTI) data base - signatures and radar track data. A major problem in HRRR ATR is the computational load created by having to match observations against target models for every feasible pose. If pose could be estimated efficiently by a filtering algorithm from track data, the ATR search space would be greatly reduced. On the other hand, HRRR ATR algorithms produce useful information about pose which could potentially aid the track-filtering process as well. We have successfully demonstrated the former concept of 'loose integration' integrating the tracker and classifier for three different type of targets moving on 2D tracks.