Track initiation in dense clutter can result in severe algorithm runtime performance degradation, particularly when using advanced tracking algorithms such as the Multiple-Frame Assignment (MFA) tracker. This is due to the exponential growth in the number of initiation hypotheses to be considered as the initiation window length increases. However, longer track initiation windows produce significantly improved track association. In balancing the need for robust track initiation with real-world runtime constraints, several possible approaches might be considered. This paper discusses basic single and multiple-sensor infrared clutter rejection techniques, and then goes on to discuss integration of those techniques with a full measurement preprocessing stage suitable for use with pixel cluster
decomposition and group tracking frameworks. Clutter rejection processing inherently overlaps the track initiation function; in both cases, candidate measurement sequences (arcs) are developed that then undergo some form of batch estimation. In considering clutter rejection at the same time as pixel processing, we note that uncertainty exists in the validity of the measurement (whether or not the measurement is of a clutter point or a true target), in the measurement state (position and intensity), and in the degree of resolution (whether a measurement represents one underlying object, or multiple). An integrated clutter rejection and pixel processing subsystem must take into account all of these processes in generating an accurate sequence of measurement frames, while minimizing the amount of unrejected clutter. We present a mechanism for combining clutter rejection with focal plane processing, and provide simulation results showing the impact of clutter processing on the runtime and tracking performance of a typical space-based infrared tracking system.