Unified framework for IR target detection and tracking

Infrared target detection and tracking has received considerable attention in recent years because of the emerging importance of infrared search and track (IRST) systems as a passive sensing modality. Various forms of advanced multi-frame signal processing algorithms have been developed to address the problem of detecting low contrast targets in clutter backgrounds. Advanced tracking algorithms such as multiple hypothesis tracking and track-before-detect are also being studied to improve overall system performance. While different algorithms have been derived in different contexts, they may be profitably examined within a common framework. This paper examines the problem of JR detection and tracking within the general framework of Bayesian hypothesis testing, and develops different detection and tracking algorithms as solutions when various simplifying assumptions are made. This exercise lends insight into the inter-relationships among different algorithms, and facilitates comparison of their strengths and weaknesses.