Proc. SPIE. 5913, Signal and Data Processing of Small Targets 2005
KEYWORDS: Infrared search and track, Infrared sensors, Point spread functions, Detection and tracking algorithms, Sensors, Satellites, Image processing, Data processing, Signal processing, Infrared radiation
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.
The observation of closely-spaced objects using limited-resolution
Infrared (IR) sensor systems can result in merged object measurements on the focal plane. These Unresolved Closely-Spaced Objects (UCSOs) can significantly hamper the performance of surveillance systems. Algorithms are desired which robustly resolve UCSO signals such that (1) the number of targets, (2) the target locations on the focal plane, (3) the uncertainty in the location estimates, and (4) the target intensity signals are correctly preserved in the resolution process. This paper presents a framework for obtaining UCSO resolution while meeting tracker real-time computing requirements by applying processing algorithms in a hierarchical fashion. Image restoration techniques, which are often quite cheap, will be applied first to help reduce noise and improve resolution of UCSO objects on the focal plane. The CLEAN algorithm, developed to restore images of point targets, is used for illustration. Then, when processor constraints allow, more intensive algorithms are applied to further resolve USCO objects. A novel pixel-cluster decomposition algorithm that uses a particle distribution representative of the pixel-cluster intensities to feed the Expectation Maximization (EM) is used in this work. We will present simulation studies that illustrate the capability of this framework to improve correct object count on the focal plane while meeting the four goals listed above. In the presence of processing time constraints, the hierarchical framework provides an interruptible mechanism which can satisfy real-time run-time constraints while improving tracking performance.