Detecting and tracking moving targets in synthetic aperture radar (SAR) data is a challenging task, demanding state-of-the-art processing methods and advanced SAR systems. Current approaches concentrate on the problem of either endo-clutter moving target tracking or exo-clutter moving target tracking, neglecting the advantages of a joint tracking framework. We present an approach relying on a combined exo- and endo-clutter processing scheme using SAR data with a high pulse repetition frequency. The main processing chain is subdivided into four major steps: 1) focusing of temporal and spatial overlapping SAR images; 2) extracting image statistics for each of these subaperture images in the endo- and exo-clutter domains; 3) subsequent tracking of both endo- and exo-clutter observations using multitarget unscented Kalman filtering; and 4) calculating real-world speeds and positions from the SAR image space coordinates using a road network. The results of this approach are validated and compared with ground-based measurements, and it is found that 100% of the vehicles were detected correctly with an accuracy in speed of 0.02±0.31 m/s and an average tracking time of ~28 s.