Advanced Algorithms for Data Fusion with 3-D Image Reconstruction from

Creating a real-time single integrated missile picture for defense against enemy missile attacks presents technical challenges due to both sensor measurement errors and discrimination uncertainties that arise from the nature of missile object characteristics and signatures. Successful intercept of lethal objects is critically degraded if uncertainties preclude the effective discrimination of lethal vs. non-lethal objects. A promising approach to reduce the effects of these uncertainties and enhance threat object discrimination is the use of 3-D object imaging techniques using all of the features and signature data provided from the network of distributed sensors. An integrated robust fusion process can enhance discrimination by considering the "cross correlations" that exist between data from multiple, geographically diverse networked sensors. To achieve this, the fusion process must be capable of fusing dissimilar data including radar feature data and thermal imagery from sources such as on-board IR sensors and SBIRS. An effective fusion approach that includes 3-D imaging must fit within processor throughput and network communication bandwidth constraints. Thus, a key consideration in multi-sensor fusion using distributed data is the Value of Information (VOI) of reported data and cost in terms of processor and network resources required to collect and disseminate the data.