Integrating drones into the civil airspace is one of the biggest challenges for civil aviation, responsible authorities and involved companies around the world in the upcoming years. For a full integration into non-segregated airspace such a system has to provide the capability to automatically detect and avoid other airspace users. In this work, we present an experimental detect and avoid system integrated into an aircraft to detect and track other aerial objects. The system is based on a custom aircraft nose-pod with two integrated cameras and several additional sensors. First test flights were successfully completed where a dataset of artificial collision scenarios executed by two aircraft was recorded. Based on this dataset a tracking framework is developed. The measurements from multiple detectors are fused onto a virtual sphere centered at the own-ship position. To reduce false tracks from ground clutter, image artifacts, clouds or dirt on the lens, a hierarchical multi-layer filter pipeline is applied. The aerial object tracking framework is evaluated on various scenarios from the challenging dataset. As the results show, aerial objects are successfully detected and tracked at large distances, even during dynamic flight of the own-ship, with heavy lens flare in the images or in front of terrain.