Unification of detection, tracking, and recognition for millimeter wave and infrared sensors
Aaron D. Lanterman, Michael I. Miller, Donald L. Snyder · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
Our pattern theoretic approach to the automated understanding of complex scenes brings the traditionally separate endeavors of detection, tracking, and recongition together into a unified jump-diffusion process. Concentrating on an air-to-ground scenario, we postulate data likelihood models for a low-resolution, wide field-of-view millimeter wave radar (for detection) and a high-resolution, narrow field-of-view forward-looking infrared sensor (for recognition). The interaction between the sensors is governed by a jump-diffusion process which provides a mathematical foundation for saccadic detection and computationally efficient target hypothesis during recognition. New objects are detected and object types are recognition through discrete jump moves. Between jumps, the location and orientation of objects are estimated via continuous diffusions. The methodology outlined may be applied to any scenario involving the fusion of low-resolution and high-resolution sensor data.