Advanced driver assistance and automated driving systems rely on an enhanced perception, which requires a reliable fusion of heterogeneous data from multiple sensors. Track-to-track fusion is a commonly used architecture in automotive perception systems. However, track management is still under-discussed among other modules of track-to-track fusion, i.e., association and state fusion, despite its significant impact on the fidelity of the fused data. Track management is responsible for maintaining the fused track list by handling appearing and disappearing objects and, more importantly, determining if a sensor track was generated by a real target or false detections. In this paper, we compare different track management strategies using simulated and real data from a radar-camera sensor cluster, analyzing their false alarm filtering effectiveness.