CSAOB’s goal is to create an accurate, high precision, redundant or uncooperative vehicle tracking and identification method. This method will reduce uncertainty, significantly improving the accuracy of ADS-B data and enable significantly reduced spacing and separations. ┬áBy enabling technology for autonomous operations and delegated separation will demonstrate the safety and efficiency of civil aviation by enabling on operational transformation and applications. We also want the system to be extensible to enhanced vision and identification in addition to detection.

Our approach will consist of an N-numbered camera sensor array that provides significantly greater resolution than human visual acuity and couple to image object detection processes for track identification. The innovation is creating a unified image for human consumption, merging the N-array image object detection tracks, adding contextual information, and human user training, and creating a user interface for Bayesian fusion to create an IAS with superior false alarm rejection and minimal missed detection.