Tracking midcourse objects in multiple IR-sensor environments is a significant and difficult scientific problem that must be solved to provide a consistent set of tracks to discrimination. For IR sensors, the resolution is limited due to the geometry and distance from the sensors to the targets. Viewed on the focal plane for a single IR sensor, the targets appear to transition from an unresolved phase (merged measurements) involving pixel-clusters into a mostly resolved phase through a possibly long partially unresolved phase. What is more, targets can appear in different resolution phases at the same time for different sensors. These resolution problems make multi-sensor tracking most difficult. Considering a centralized multi-sensor tracking architecture we discuss robust methods for identification of merged measurements at the fusion node and develop a method for pixel-cluster decomposition that allows the tracking system to re-process focal-plane image data for improved tracking performance. The resulting system can avoid inconsistent measurement data at the fusion node. We then present a more general multiple hypothesis pixel-cluster decomposition approach based on finding k-best assignments and solving a number of $n$-dimensional assignment problems over n frames to find a decomposition among several pixel-cluster decomposition hypotheses that best represents a frame of data based on the information from n frames of data.

Date Published: 5 January 2004
PDF: 13 pages
Proc. SPIE 5204, Signal and Data Processing of Small Targets 2003, (5 January 2004);

Published in SPIE Proceedings Vol. 5204:
Signal and Data Processing of Small Targets 2003
Oliver E. Drummond, Editor(s)