Wide area motion imagery (WAMI) acquired by an airborne sensor enables continuous monitoring of large urban areas. Reliable vehicle tracking in this imagery remains challenging due to low frame rate and small object size. Many approaches solely rely on motion detections provided by frame differencing or background subtraction. Recent approaches for persistent tracking, i.e. tracking vehicles even if they become stationary, compensate for missing motion detections by combining a detection-based tracker with a second tracker based on appearance or local context. We propose a novel single tracker framework based on multiple hypothesis tracking (MHT) that enables persistent tracking in WAMI data by recovering missing motion detections with a classifier-based detector, thus avoiding the additional complexity introduced by combining two trackers. We adapt the MHT approach to the specific context of WAMI tracking by integrating an appearance-based similarity measure, vehicle-collision tests, and clutter handling. An evaluation on a region of interest in the WPAFB 2009 dataset shows state-of-the-art performance.